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2025-04-11 00:00:00
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2406.07266
|
Ross Irwin
|
Ross Irwin, Alessandro Tibo, Jon Paul Janet and Simon Olsson
|
SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and
Equivariant Flow Matching
|
AISTATS 2025
| null | null | null |
cs.LG cs.AI cs.NE
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Methods for jointly generating molecular graphs along with their 3D
conformations have gained prominence recently due to their potential impact on
structure-based drug design. Current approaches, however, often suffer from
very slow sampling times or generate molecules with poor chemical validity.
Addressing these limitations, we propose Semla, a scalable E(3)-equivariant
message passing architecture. We further introduce an unconditional 3D
molecular generation model, SemlaFlow, which is trained using equivariant flow
matching to generate a joint distribution over atom types, coordinates, bond
types and formal charges. Our model produces state-of-the-art results on
benchmark datasets with as few as 20 sampling steps, corresponding to a two
order-of-magnitude speedup compared to state-of-the-art. Furthermore, we
highlight limitations of current evaluation methods for 3D generation and
propose new benchmark metrics for unconditional molecular generators. Finally,
using these new metrics, we compare our model's ability to generate high
quality samples against current approaches and further demonstrate SemlaFlow's
strong performance.
|
[
{
"version": "v1",
"created": "Tue, 11 Jun 2024 13:51:51 GMT"
},
{
"version": "v2",
"created": "Tue, 25 Jun 2024 11:42:09 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 16:56:08 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Irwin",
"Ross",
""
],
[
"Tibo",
"Alessandro",
""
],
[
"Janet",
"Jon Paul",
""
],
[
"Olsson",
"Simon",
""
]
] |
TITLE: SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and
Equivariant Flow Matching
ABSTRACT: Methods for jointly generating molecular graphs along with their 3D
conformations have gained prominence recently due to their potential impact on
structure-based drug design. Current approaches, however, often suffer from
very slow sampling times or generate molecules with poor chemical validity.
Addressing these limitations, we propose Semla, a scalable E(3)-equivariant
message passing architecture. We further introduce an unconditional 3D
molecular generation model, SemlaFlow, which is trained using equivariant flow
matching to generate a joint distribution over atom types, coordinates, bond
types and formal charges. Our model produces state-of-the-art results on
benchmark datasets with as few as 20 sampling steps, corresponding to a two
order-of-magnitude speedup compared to state-of-the-art. Furthermore, we
highlight limitations of current evaluation methods for 3D generation and
propose new benchmark metrics for unconditional molecular generators. Finally,
using these new metrics, we compare our model's ability to generate high
quality samples against current approaches and further demonstrate SemlaFlow's
strong performance.
|
no_new_dataset
| 0.95297
|
2406.10288
|
Francisco Eiras
|
Francisco Eiras, Aleksandar Petrov, Philip H.S. Torr, M. Pawan Kumar,
Adel Bibi
|
Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large
Language Models
|
Accepted to ICLR'25
| null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Recent research shows that fine-tuning on benign instruction-following data
can inadvertently undo the safety alignment process and increase a model's
propensity to comply with harmful queries. While instruction-following
fine-tuning is important, task-specific fine-tuning - where models are trained
on datasets with clear ground truth answers (e.g., multiple choice questions) -
can enhance model performance on specialized downstream tasks. Understanding
and mitigating safety risks in the task-specific setting remains distinct from
the instruction-following context due to structural differences in the data.
Our work demonstrates how malicious actors can subtly manipulate the structure
of almost any task-specific dataset to foster significantly more dangerous
model behaviors, while maintaining an appearance of innocuity and reasonable
downstream task performance. To address this issue, we propose a novel
mitigation strategy that mixes in safety data which mimics the task format and
prompting style of the user data, showing this is significantly more effective
and efficient than existing baselines at re-establishing safety alignment while
maintaining similar task performance.
|
[
{
"version": "v1",
"created": "Wed, 12 Jun 2024 18:33:11 GMT"
},
{
"version": "v2",
"created": "Mon, 1 Jul 2024 10:17:58 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 11:36:06 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Eiras",
"Francisco",
""
],
[
"Petrov",
"Aleksandar",
""
],
[
"Torr",
"Philip H. S.",
""
],
[
"Kumar",
"M. Pawan",
""
],
[
"Bibi",
"Adel",
""
]
] |
TITLE: Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large
Language Models
ABSTRACT: Recent research shows that fine-tuning on benign instruction-following data
can inadvertently undo the safety alignment process and increase a model's
propensity to comply with harmful queries. While instruction-following
fine-tuning is important, task-specific fine-tuning - where models are trained
on datasets with clear ground truth answers (e.g., multiple choice questions) -
can enhance model performance on specialized downstream tasks. Understanding
and mitigating safety risks in the task-specific setting remains distinct from
the instruction-following context due to structural differences in the data.
Our work demonstrates how malicious actors can subtly manipulate the structure
of almost any task-specific dataset to foster significantly more dangerous
model behaviors, while maintaining an appearance of innocuity and reasonable
downstream task performance. To address this issue, we propose a novel
mitigation strategy that mixes in safety data which mimics the task format and
prompting style of the user data, showing this is significantly more effective
and efficient than existing baselines at re-establishing safety alignment while
maintaining similar task performance.
|
no_new_dataset
| 0.946101
|
2406.11451
|
Jiawei Chen
|
Yue Jiang, Jiawei Chen, Dingkang Yang, Mingcheng Li, Shunli Wang, Tong
Wu, Ke Li, Lihua Zhang
|
CoMT: Chain-of-Medical-Thought Reduces Hallucination in Medical Report
Generation
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Automatic medical report generation (MRG), which possesses significant
research value as it can aid radiologists in clinical diagnosis and report
composition, has garnered increasing attention. Despite recent progress,
generating accurate reports remains arduous due to the requirement for precise
clinical comprehension and disease diagnosis inference. Furthermore, owing to
the limited accessibility of medical data and the imbalanced distribution of
diseases, the underrepresentation of rare diseases in training data makes
large-scale medical visual language models (LVLMs) prone to hallucinations,
such as omissions or fabrications, severely undermining diagnostic performance
and further intensifying the challenges for MRG in practice. In this study, to
effectively mitigate hallucinations in medical report generation, we propose a
chain-of-medical-thought approach (CoMT), which intends to imitate the
cognitive process of human doctors by decomposing diagnostic procedures. The
radiological features with different importance are structured into
fine-grained medical thought chains to enhance the inferential ability during
diagnosis, thereby alleviating hallucination problems and enhancing the
diagnostic accuracy of MRG. The code and dataset have been released at
https://github.com/FRENKIE-CHIANG/CoMT.
|
[
{
"version": "v1",
"created": "Mon, 17 Jun 2024 12:03:32 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jun 2024 14:20:46 GMT"
},
{
"version": "v3",
"created": "Wed, 18 Sep 2024 06:53:40 GMT"
},
{
"version": "v4",
"created": "Fri, 28 Feb 2025 03:36:50 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Jiang",
"Yue",
""
],
[
"Chen",
"Jiawei",
""
],
[
"Yang",
"Dingkang",
""
],
[
"Li",
"Mingcheng",
""
],
[
"Wang",
"Shunli",
""
],
[
"Wu",
"Tong",
""
],
[
"Li",
"Ke",
""
],
[
"Zhang",
"Lihua",
""
]
] |
TITLE: CoMT: Chain-of-Medical-Thought Reduces Hallucination in Medical Report
Generation
ABSTRACT: Automatic medical report generation (MRG), which possesses significant
research value as it can aid radiologists in clinical diagnosis and report
composition, has garnered increasing attention. Despite recent progress,
generating accurate reports remains arduous due to the requirement for precise
clinical comprehension and disease diagnosis inference. Furthermore, owing to
the limited accessibility of medical data and the imbalanced distribution of
diseases, the underrepresentation of rare diseases in training data makes
large-scale medical visual language models (LVLMs) prone to hallucinations,
such as omissions or fabrications, severely undermining diagnostic performance
and further intensifying the challenges for MRG in practice. In this study, to
effectively mitigate hallucinations in medical report generation, we propose a
chain-of-medical-thought approach (CoMT), which intends to imitate the
cognitive process of human doctors by decomposing diagnostic procedures. The
radiological features with different importance are structured into
fine-grained medical thought chains to enhance the inferential ability during
diagnosis, thereby alleviating hallucination problems and enhancing the
diagnostic accuracy of MRG. The code and dataset have been released at
https://github.com/FRENKIE-CHIANG/CoMT.
|
new_dataset
| 0.944638
|
2406.14045
|
Yu-Neng Chuang
|
Yu-Neng Chuang, Songchen Li, Jiayi Yuan, Guanchu Wang, Kwei-Herng Lai,
Songyuan Sui, Leisheng Yu, Sirui Ding, Chia-Yuan Chang, Qiaoyu Tan, Daochen
Zha, Xia Hu
|
LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time
Series Forecasting
| null | null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Time Series Forecasting (TSF) has long been a challenge in time series
analysis. Inspired by the success of Large Language Models (LLMs), researchers
are now developing Large Time Series Models (LTSMs)-universal transformer-based
models that use autoregressive prediction-to improve TSF. However, training
LTSMs on heterogeneous time series data poses unique challenges, including
diverse frequencies, dimensions, and patterns across datasets. Recent endeavors
have studied and evaluated various design choices aimed at enhancing LTSM
training and generalization capabilities. However, these design choices are
typically studied and evaluated in isolation and are not benchmarked
collectively. In this work, we introduce LTSM-Bundle, a comprehensive toolbox,
and benchmark for training LTSMs, spanning pre-processing techniques, model
configurations, and dataset configuration. It modularized and benchmarked LTSMs
from multiple dimensions, encompassing prompting strategies, tokenization
approaches, training paradigms, base model selection, data quantity, and
dataset diversity. Furthermore, we combine the most effective design choices
identified in our study. Empirical results demonstrate that this combination
achieves superior zero-shot and few-shot performances compared to
state-of-the-art LTSMs and traditional TSF methods on benchmark datasets.
|
[
{
"version": "v1",
"created": "Thu, 20 Jun 2024 07:09:19 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Feb 2025 23:12:38 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Chuang",
"Yu-Neng",
""
],
[
"Li",
"Songchen",
""
],
[
"Yuan",
"Jiayi",
""
],
[
"Wang",
"Guanchu",
""
],
[
"Lai",
"Kwei-Herng",
""
],
[
"Sui",
"Songyuan",
""
],
[
"Yu",
"Leisheng",
""
],
[
"Ding",
"Sirui",
""
],
[
"Chang",
"Chia-Yuan",
""
],
[
"Tan",
"Qiaoyu",
""
],
[
"Zha",
"Daochen",
""
],
[
"Hu",
"Xia",
""
]
] |
TITLE: LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time
Series Forecasting
ABSTRACT: Time Series Forecasting (TSF) has long been a challenge in time series
analysis. Inspired by the success of Large Language Models (LLMs), researchers
are now developing Large Time Series Models (LTSMs)-universal transformer-based
models that use autoregressive prediction-to improve TSF. However, training
LTSMs on heterogeneous time series data poses unique challenges, including
diverse frequencies, dimensions, and patterns across datasets. Recent endeavors
have studied and evaluated various design choices aimed at enhancing LTSM
training and generalization capabilities. However, these design choices are
typically studied and evaluated in isolation and are not benchmarked
collectively. In this work, we introduce LTSM-Bundle, a comprehensive toolbox,
and benchmark for training LTSMs, spanning pre-processing techniques, model
configurations, and dataset configuration. It modularized and benchmarked LTSMs
from multiple dimensions, encompassing prompting strategies, tokenization
approaches, training paradigms, base model selection, data quantity, and
dataset diversity. Furthermore, we combine the most effective design choices
identified in our study. Empirical results demonstrate that this combination
achieves superior zero-shot and few-shot performances compared to
state-of-the-art LTSMs and traditional TSF methods on benchmark datasets.
|
no_new_dataset
| 0.943191
|
2406.18450
|
Aliz\'ee Pace
|
Aliz\'ee Pace, Bernhard Sch\"olkopf, Gunnar R\"atsch, Giorgia Ramponi
|
Preference Elicitation for Offline Reinforcement Learning
|
ICLR 2025
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Applying reinforcement learning (RL) to real-world problems is often made
challenging by the inability to interact with the environment and the
difficulty of designing reward functions. Offline RL addresses the first
challenge by considering access to an offline dataset of environment
interactions labeled by the reward function. In contrast, Preference-based RL
does not assume access to the reward function and learns it from preferences,
but typically requires an online interaction with the environment. We bridge
the gap between these frameworks by exploring efficient methods for acquiring
preference feedback in a fully offline setup. We propose Sim-OPRL, an offline
preference-based reinforcement learning algorithm, which leverages a learned
environment model to elicit preference feedback on simulated rollouts. Drawing
on insights from both the offline RL and the preference-based RL literature,
our algorithm employs a pessimistic approach for out-of-distribution data, and
an optimistic approach for acquiring informative preferences about the optimal
policy. We provide theoretical guarantees regarding the sample complexity of
our approach, dependent on how well the offline data covers the optimal policy.
Finally, we demonstrate the empirical performance of Sim-OPRL in various
environments.
|
[
{
"version": "v1",
"created": "Wed, 26 Jun 2024 15:59:13 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 08:36:00 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Pace",
"Alizée",
""
],
[
"Schölkopf",
"Bernhard",
""
],
[
"Rätsch",
"Gunnar",
""
],
[
"Ramponi",
"Giorgia",
""
]
] |
TITLE: Preference Elicitation for Offline Reinforcement Learning
ABSTRACT: Applying reinforcement learning (RL) to real-world problems is often made
challenging by the inability to interact with the environment and the
difficulty of designing reward functions. Offline RL addresses the first
challenge by considering access to an offline dataset of environment
interactions labeled by the reward function. In contrast, Preference-based RL
does not assume access to the reward function and learns it from preferences,
but typically requires an online interaction with the environment. We bridge
the gap between these frameworks by exploring efficient methods for acquiring
preference feedback in a fully offline setup. We propose Sim-OPRL, an offline
preference-based reinforcement learning algorithm, which leverages a learned
environment model to elicit preference feedback on simulated rollouts. Drawing
on insights from both the offline RL and the preference-based RL literature,
our algorithm employs a pessimistic approach for out-of-distribution data, and
an optimistic approach for acquiring informative preferences about the optimal
policy. We provide theoretical guarantees regarding the sample complexity of
our approach, dependent on how well the offline data covers the optimal policy.
Finally, we demonstrate the empirical performance of Sim-OPRL in various
environments.
|
no_new_dataset
| 0.945701
|
2407.02447
|
Anshul Nasery
|
Anshul Nasery, Jonathan Hayase, Pang Wei Koh, Sewoong Oh
|
PLeaS -- Merging Models with Permutations and Least Squares
|
Accepted to CVPR 2025
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The democratization of machine learning systems has made the process of
fine-tuning accessible to practitioners, leading to a wide range of open-source
models fine-tuned on specialized tasks and datasets. Recent work has proposed
to merge such models to combine their functionalities. However, prior
approaches are usually restricted to models that are fine-tuned from the same
base model. Furthermore, the final merged model is typically required to be of
the same size as the original models. In this work, we propose a new two-step
algorithm to merge models -- termed PLeaS -- which relaxes these constraints.
First, leveraging the Permutation symmetries inherent in the two models, PLeaS
partially matches nodes in each layer by maximizing alignment. Next, PLeaS
computes the weights of the merged model as a layer-wise Least Squares solution
to minimize the approximation error between the features of the merged model
and the permuted features of the original models. PLeaS allows a practitioner
to merge two models sharing the same architecture into a single performant
model of a desired size, even when the two original models are fine-tuned from
different base models. We also demonstrate how our method can be extended to
address a challenging scenario where no data is available from the fine-tuning
domains. We demonstrate our method to merge ResNet and ViT models trained with
shared and different label spaces, and show improvement over the
state-of-the-art merging methods of up to 15 percentage points for the same
target compute while merging models trained on DomainNet and fine-grained
classification tasks. Our code is open-sourced at
https://github.com/SewoongLab/PLeaS-Merging .
|
[
{
"version": "v1",
"created": "Tue, 2 Jul 2024 17:24:04 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Feb 2025 22:26:01 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Nasery",
"Anshul",
""
],
[
"Hayase",
"Jonathan",
""
],
[
"Koh",
"Pang Wei",
""
],
[
"Oh",
"Sewoong",
""
]
] |
TITLE: PLeaS -- Merging Models with Permutations and Least Squares
ABSTRACT: The democratization of machine learning systems has made the process of
fine-tuning accessible to practitioners, leading to a wide range of open-source
models fine-tuned on specialized tasks and datasets. Recent work has proposed
to merge such models to combine their functionalities. However, prior
approaches are usually restricted to models that are fine-tuned from the same
base model. Furthermore, the final merged model is typically required to be of
the same size as the original models. In this work, we propose a new two-step
algorithm to merge models -- termed PLeaS -- which relaxes these constraints.
First, leveraging the Permutation symmetries inherent in the two models, PLeaS
partially matches nodes in each layer by maximizing alignment. Next, PLeaS
computes the weights of the merged model as a layer-wise Least Squares solution
to minimize the approximation error between the features of the merged model
and the permuted features of the original models. PLeaS allows a practitioner
to merge two models sharing the same architecture into a single performant
model of a desired size, even when the two original models are fine-tuned from
different base models. We also demonstrate how our method can be extended to
address a challenging scenario where no data is available from the fine-tuning
domains. We demonstrate our method to merge ResNet and ViT models trained with
shared and different label spaces, and show improvement over the
state-of-the-art merging methods of up to 15 percentage points for the same
target compute while merging models trained on DomainNet and fine-grained
classification tasks. Our code is open-sourced at
https://github.com/SewoongLab/PLeaS-Merging .
|
no_new_dataset
| 0.948058
|
2407.08351
|
Xiang Lisa Li
|
Xiang Lisa Li, Farzaan Kaiyom, Evan Zheran Liu, Yifan Mai, Percy
Liang, Tatsunori Hashimoto
|
AutoBencher: Towards Declarative Benchmark Construction
|
Accepted for publication at ICLR 2025
| null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We present AutoBencher, a declarative framework for automatic benchmark
construction, and use it to scalably discover novel insights and
vulnerabilities of existing language models. Concretely, given a few desiderata
of benchmarks (e.g., question difficulty, topic salience), we operationalize
each desideratum and cast benchmark creation as an optimization problem.
Specifically, we experiment with two settings with different optimization
objectives: (i) for capability evaluation, we declare the goal of finding a
salient, difficult dataset that induces novel performance patterns; (ii) for
safety evaluation, we declare the goal of finding a dataset of unsafe prompts
that existing LMs fail to decline. To tackle this optimization problem, we use
a language model to iteratively propose and refine dataset descriptions, which
are then used to generate topic-specific questions and answers. These
descriptions are optimized to improve the declared desiderata. We use
AutoBencher (powered by GPT-4) to create datasets for math, multilinguality,
knowledge, and safety. The scalability of AutoBencher allows it to test
fine-grained categories and tail knowledge, creating datasets that elicit 22%
more model errors (i.e., difficulty) than existing benchmarks. On the novelty
ends, AutoBencher also helps identify specific gaps not captured by existing
benchmarks: e.g., Gemini-Pro has knowledge gaps on Permian Extinction and
Fordism while GPT-4o fails to decline harmful requests about cryptocurrency
scams.
|
[
{
"version": "v1",
"created": "Thu, 11 Jul 2024 10:03:47 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 08:14:49 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Li",
"Xiang Lisa",
""
],
[
"Kaiyom",
"Farzaan",
""
],
[
"Liu",
"Evan Zheran",
""
],
[
"Mai",
"Yifan",
""
],
[
"Liang",
"Percy",
""
],
[
"Hashimoto",
"Tatsunori",
""
]
] |
TITLE: AutoBencher: Towards Declarative Benchmark Construction
ABSTRACT: We present AutoBencher, a declarative framework for automatic benchmark
construction, and use it to scalably discover novel insights and
vulnerabilities of existing language models. Concretely, given a few desiderata
of benchmarks (e.g., question difficulty, topic salience), we operationalize
each desideratum and cast benchmark creation as an optimization problem.
Specifically, we experiment with two settings with different optimization
objectives: (i) for capability evaluation, we declare the goal of finding a
salient, difficult dataset that induces novel performance patterns; (ii) for
safety evaluation, we declare the goal of finding a dataset of unsafe prompts
that existing LMs fail to decline. To tackle this optimization problem, we use
a language model to iteratively propose and refine dataset descriptions, which
are then used to generate topic-specific questions and answers. These
descriptions are optimized to improve the declared desiderata. We use
AutoBencher (powered by GPT-4) to create datasets for math, multilinguality,
knowledge, and safety. The scalability of AutoBencher allows it to test
fine-grained categories and tail knowledge, creating datasets that elicit 22%
more model errors (i.e., difficulty) than existing benchmarks. On the novelty
ends, AutoBencher also helps identify specific gaps not captured by existing
benchmarks: e.g., Gemini-Pro has knowledge gaps on Permian Extinction and
Fordism while GPT-4o fails to decline harmful requests about cryptocurrency
scams.
|
no_new_dataset
| 0.773088
|
2407.14543
|
Micha{\l} Kozielski
|
Micha{\l} Kozielski, Marek Sikora, {\L}ukasz Wawrowski
|
Towards consistency of rule-based explainer and black box model --
fusion of rule induction and XAI-based feature importance
| null | null |
10.1016/j.knosys.2025.113092
| null |
cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Rule-based models offer a human-understandable representation, i.e. they are
interpretable. For this reason, they are used to explain the decisions of
non-interpretable complex models, referred to as black box models. The
generation of such explanations involves the approximation of a black box model
by a rule-based model. To date, however, it has not been investigated whether
the rule-based model makes decisions in the same way as the black box model it
approximates. Decision making in the same way is understood in this work as the
consistency of decisions and the consistency of the most important attributes
used for decision making. This study proposes a novel approach ensuring that
the rule-based surrogate model mimics the performance of the black box model.
The proposed solution performs an explanation fusion involving rule generation
and taking into account the feature importance determined by the selected XAI
methods for the black box model being explained. The result of the method can
be both global and local rule-based explanations. The quality of the proposed
solution was verified by extensive analysis on 30 tabular benchmark datasets
representing classification problems. Evaluation included comparison with the
reference method and an illustrative case study. In addition, the paper
discusses the possible pathways for the application of the rule-based approach
in XAI and how rule-based explanations, including the proposed method, meet the
user perspective and requirements for both content and presentation. The
software created and a detailed report containing the full experimental results
are available on the GitHub repository
(https://github.com/ruleminer/FI-rules4XAI ).
|
[
{
"version": "v1",
"created": "Tue, 16 Jul 2024 07:56:29 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Kozielski",
"Michał",
""
],
[
"Sikora",
"Marek",
""
],
[
"Wawrowski",
"Łukasz",
""
]
] |
TITLE: Towards consistency of rule-based explainer and black box model --
fusion of rule induction and XAI-based feature importance
ABSTRACT: Rule-based models offer a human-understandable representation, i.e. they are
interpretable. For this reason, they are used to explain the decisions of
non-interpretable complex models, referred to as black box models. The
generation of such explanations involves the approximation of a black box model
by a rule-based model. To date, however, it has not been investigated whether
the rule-based model makes decisions in the same way as the black box model it
approximates. Decision making in the same way is understood in this work as the
consistency of decisions and the consistency of the most important attributes
used for decision making. This study proposes a novel approach ensuring that
the rule-based surrogate model mimics the performance of the black box model.
The proposed solution performs an explanation fusion involving rule generation
and taking into account the feature importance determined by the selected XAI
methods for the black box model being explained. The result of the method can
be both global and local rule-based explanations. The quality of the proposed
solution was verified by extensive analysis on 30 tabular benchmark datasets
representing classification problems. Evaluation included comparison with the
reference method and an illustrative case study. In addition, the paper
discusses the possible pathways for the application of the rule-based approach
in XAI and how rule-based explanations, including the proposed method, meet the
user perspective and requirements for both content and presentation. The
software created and a detailed report containing the full experimental results
are available on the GitHub repository
(https://github.com/ruleminer/FI-rules4XAI ).
|
no_new_dataset
| 0.950915
|
2407.17396
|
Irtaza Khalid
|
Irtaza Khalid, Steven Schockaert
|
Systematic Relational Reasoning With Epistemic Graph Neural Networks
|
10+29 pages, 5+13 figures, 4+10 tables. Comments welcome!
|
ICLR 2025 main
| null | null |
cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Developing models that can learn to reason is a notoriously challenging
problem. We focus on reasoning in relational domains, where the use of Graph
Neural Networks (GNNs) seems like a natural choice. However, previous work has
shown that regular GNNs lack the ability to systematically generalize from
training examples on test graphs requiring longer inference chains, which
fundamentally limits their reasoning abilities. A common solution relies on
neuro-symbolic methods that systematically reason by learning rules, but their
scalability is often limited and they tend to make unrealistically strong
assumptions, e.g.\ that the answer can always be inferred from a single
relational path. We propose the Epistemic GNN (EpiGNN), a novel
parameter-efficient and scalable GNN architecture with an epistemic inductive
bias for systematic reasoning. Node embeddings in EpiGNNs are treated as
epistemic states, and message passing is implemented accordingly. We show that
EpiGNNs achieve state-of-the-art results on link prediction tasks that require
systematic reasoning. Furthermore, for inductive knowledge graph completion,
EpiGNNs rival the performance of state-of-the-art specialized approaches.
Finally, we introduce two new benchmarks that go beyond standard relational
reasoning by requiring the aggregation of information from multiple paths.
Here, existing neuro-symbolic approaches fail, yet EpiGNNs learn to reason
accurately. Code and datasets are available at
https://github.com/erg0dic/gnn-sg.
|
[
{
"version": "v1",
"created": "Wed, 24 Jul 2024 16:17:15 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Feb 2025 22:50:41 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Khalid",
"Irtaza",
""
],
[
"Schockaert",
"Steven",
""
]
] |
TITLE: Systematic Relational Reasoning With Epistemic Graph Neural Networks
ABSTRACT: Developing models that can learn to reason is a notoriously challenging
problem. We focus on reasoning in relational domains, where the use of Graph
Neural Networks (GNNs) seems like a natural choice. However, previous work has
shown that regular GNNs lack the ability to systematically generalize from
training examples on test graphs requiring longer inference chains, which
fundamentally limits their reasoning abilities. A common solution relies on
neuro-symbolic methods that systematically reason by learning rules, but their
scalability is often limited and they tend to make unrealistically strong
assumptions, e.g.\ that the answer can always be inferred from a single
relational path. We propose the Epistemic GNN (EpiGNN), a novel
parameter-efficient and scalable GNN architecture with an epistemic inductive
bias for systematic reasoning. Node embeddings in EpiGNNs are treated as
epistemic states, and message passing is implemented accordingly. We show that
EpiGNNs achieve state-of-the-art results on link prediction tasks that require
systematic reasoning. Furthermore, for inductive knowledge graph completion,
EpiGNNs rival the performance of state-of-the-art specialized approaches.
Finally, we introduce two new benchmarks that go beyond standard relational
reasoning by requiring the aggregation of information from multiple paths.
Here, existing neuro-symbolic approaches fail, yet EpiGNNs learn to reason
accurately. Code and datasets are available at
https://github.com/erg0dic/gnn-sg.
|
no_new_dataset
| 0.938632
|
2407.17470
|
Yiming Xie
|
Yiming Xie, Chun-Han Yao, Vikram Voleti, Huaizu Jiang, Varun Jampani
|
SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View
Consistency
|
Project page: https://sv4d.github.io/
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We present Stable Video 4D (SV4D), a latent video diffusion model for
multi-frame and multi-view consistent dynamic 3D content generation. Unlike
previous methods that rely on separately trained generative models for video
generation and novel view synthesis, we design a unified diffusion model to
generate novel view videos of dynamic 3D objects. Specifically, given a
monocular reference video, SV4D generates novel views for each video frame that
are temporally consistent. We then use the generated novel view videos to
optimize an implicit 4D representation (dynamic NeRF) efficiently, without the
need for cumbersome SDS-based optimization used in most prior works. To train
our unified novel view video generation model, we curate a dynamic 3D object
dataset from the existing Objaverse dataset. Extensive experimental results on
multiple datasets and user studies demonstrate SV4D's state-of-the-art
performance on novel-view video synthesis as well as 4D generation compared to
prior works.
|
[
{
"version": "v1",
"created": "Wed, 24 Jul 2024 17:59:43 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Feb 2025 21:52:39 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Xie",
"Yiming",
""
],
[
"Yao",
"Chun-Han",
""
],
[
"Voleti",
"Vikram",
""
],
[
"Jiang",
"Huaizu",
""
],
[
"Jampani",
"Varun",
""
]
] |
TITLE: SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View
Consistency
ABSTRACT: We present Stable Video 4D (SV4D), a latent video diffusion model for
multi-frame and multi-view consistent dynamic 3D content generation. Unlike
previous methods that rely on separately trained generative models for video
generation and novel view synthesis, we design a unified diffusion model to
generate novel view videos of dynamic 3D objects. Specifically, given a
monocular reference video, SV4D generates novel views for each video frame that
are temporally consistent. We then use the generated novel view videos to
optimize an implicit 4D representation (dynamic NeRF) efficiently, without the
need for cumbersome SDS-based optimization used in most prior works. To train
our unified novel view video generation model, we curate a dynamic 3D object
dataset from the existing Objaverse dataset. Extensive experimental results on
multiple datasets and user studies demonstrate SV4D's state-of-the-art
performance on novel-view video synthesis as well as 4D generation compared to
prior works.
|
no_new_dataset
| 0.945045
|
2407.20595
|
Francis Kulumba
|
Francis Kulumba, Wissam Antoun, Guillaume Vimont, Laurent Romary
|
Harvesting Textual and Structured Data from the HAL Publication
Repository
|
Under review
| null | null | null |
cs.DL cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
HAL (\textit{Hyper Articles en Ligne}) is the French national publication
repository, used by most higher education and research organizations for their
open science policy. Although it is a rich repository of academic documents,
its potential for advanced research has not been fully explored. We present
HALvest, a unique dataset that bridges the gap between citation networks and
the full text of HAL-submitted articles to help with authorship attribution and
verification. This first iteration consists of approximately 700,000 documents,
spanning 56 languages across 13 identified domains. We transform articles'
metadata into a citation network, producing a heterogeneous graph. This graph
includes uniquely identified authors on HAL, as well as all open-access
documents and their references. Finally, we mine 14.5 million high-quality
sequence pairs from HALvest for contrastive learning purposes. By providing
different views of HAL, suited for modern machine learning, we aim to assist
practitioners in better analyzing and interpreting research dynamics.
|
[
{
"version": "v1",
"created": "Tue, 30 Jul 2024 07:14:04 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Feb 2025 19:33:23 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Kulumba",
"Francis",
""
],
[
"Antoun",
"Wissam",
""
],
[
"Vimont",
"Guillaume",
""
],
[
"Romary",
"Laurent",
""
]
] |
TITLE: Harvesting Textual and Structured Data from the HAL Publication
Repository
ABSTRACT: HAL (\textit{Hyper Articles en Ligne}) is the French national publication
repository, used by most higher education and research organizations for their
open science policy. Although it is a rich repository of academic documents,
its potential for advanced research has not been fully explored. We present
HALvest, a unique dataset that bridges the gap between citation networks and
the full text of HAL-submitted articles to help with authorship attribution and
verification. This first iteration consists of approximately 700,000 documents,
spanning 56 languages across 13 identified domains. We transform articles'
metadata into a citation network, producing a heterogeneous graph. This graph
includes uniquely identified authors on HAL, as well as all open-access
documents and their references. Finally, we mine 14.5 million high-quality
sequence pairs from HALvest for contrastive learning purposes. By providing
different views of HAL, suited for modern machine learning, we aim to assist
practitioners in better analyzing and interpreting research dynamics.
|
new_dataset
| 0.959116
|
2408.08545
|
Kaushal Kumar Maurya
|
Kaushal Kumar Maurya, KV Aditya Srivatsa, Ekaterina Kochmar
|
SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language
Models
|
8 pages
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Large language models (LLMs) have been widely adopted due to their remarkable
performance across various applications, driving the accelerated development of
a large number of diverse models. However, these individual LLMs show
limitations in generalization and performance on complex tasks due to inherent
training biases, model size constraints, and the quality or diversity of
pre-training datasets. A promising direction is to efficiently harness the
diverse capabilities of LLMs to overcome these individual limitations. To
address these limitations, we introduce a novel LLM selection algorithm called
SelectLLM, which efficiently directs input queries to the most suitable subset
of LLMs from a large pool, ensuring that the selected models collectively
provide accurate responses. SelectLLM employs a multi-label classifier and
policy based on the classifier's predictions and confidence scores in selecting
an optimal, query-aware, and lightweight subset of LLMs. Our findings indicate
that the proposed model outperforms existing ensemble-based baselines and
achieves competitive performance with similarly sized top-performing LLMs while
maintaining efficiency. Specifically, it achieves a huge reduction in inference
latency on two challenging reasoning benchmarks: 13% on GSM8K and 70% on MMLU,
compared to the top-performing baseline. Also, we establish a theoretical upper
bound by an Oracle with LLMs and perform an in-depth linguistic analysis to
understand the performance gap between the Oracle and SelectLLM.
|
[
{
"version": "v1",
"created": "Fri, 16 Aug 2024 06:11:21 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Dec 2024 05:01:44 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 13:23:56 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Maurya",
"Kaushal Kumar",
""
],
[
"Srivatsa",
"KV Aditya",
""
],
[
"Kochmar",
"Ekaterina",
""
]
] |
TITLE: SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language
Models
ABSTRACT: Large language models (LLMs) have been widely adopted due to their remarkable
performance across various applications, driving the accelerated development of
a large number of diverse models. However, these individual LLMs show
limitations in generalization and performance on complex tasks due to inherent
training biases, model size constraints, and the quality or diversity of
pre-training datasets. A promising direction is to efficiently harness the
diverse capabilities of LLMs to overcome these individual limitations. To
address these limitations, we introduce a novel LLM selection algorithm called
SelectLLM, which efficiently directs input queries to the most suitable subset
of LLMs from a large pool, ensuring that the selected models collectively
provide accurate responses. SelectLLM employs a multi-label classifier and
policy based on the classifier's predictions and confidence scores in selecting
an optimal, query-aware, and lightweight subset of LLMs. Our findings indicate
that the proposed model outperforms existing ensemble-based baselines and
achieves competitive performance with similarly sized top-performing LLMs while
maintaining efficiency. Specifically, it achieves a huge reduction in inference
latency on two challenging reasoning benchmarks: 13% on GSM8K and 70% on MMLU,
compared to the top-performing baseline. Also, we establish a theoretical upper
bound by an Oracle with LLMs and perform an in-depth linguistic analysis to
understand the performance gap between the Oracle and SelectLLM.
|
no_new_dataset
| 0.943712
|
2408.14578
|
Ligao Ruan
|
Ligao Ruan, Giles Hamilton-Fletcher, Mahya Beheshti, Todd E Hudson,
Maurizio Porfiri, JR Rizzo
|
Multi-faceted Sensory Substitution for Curb Alerting: A Pilot
Investigation in Persons with Blindness and Low Vision
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Curbs -- the edge of a raised sidewalk at the point where it meets a street
-- crucial in urban environments where they help delineate safe pedestrian
zones, from dangerous vehicular lanes. However, curbs themselves are
significant navigation hazards, particularly for people who are blind or have
low vision (pBLV). The challenges faced by pBLV in detecting and properly
orientating themselves for these abrupt elevation changes can lead to falls and
serious injuries. Despite recent advancements in assistive technologies, the
detection and early warning of curbs remains a largely unsolved challenge. This
paper aims to tackle this gap by introducing a novel, multi-faceted sensory
substitution approach hosted on a smart wearable; the platform leverages an RGB
camera and an embedded system to capture and segment curbs in real time and
provide early warning and orientation information. The system utilizes YOLO
(You Only Look Once) v8 segmentation model, trained on our custom curb dataset
for the camera input. The output of the system consists of adaptive auditory
beeps, abstract sonification, and speech, conveying information about the
relative distance and orientation of curbs. Through human-subjects
experimentation, we demonstrate the effectiveness of the system as compared to
the white cane. Results show that our system can provide advanced warning
through a larger safety window than the cane, while offering nearly identical
curb orientation information.
|
[
{
"version": "v1",
"created": "Mon, 26 Aug 2024 18:52:45 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Aug 2024 14:22:22 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Ruan",
"Ligao",
""
],
[
"Hamilton-Fletcher",
"Giles",
""
],
[
"Beheshti",
"Mahya",
""
],
[
"Hudson",
"Todd E",
""
],
[
"Porfiri",
"Maurizio",
""
],
[
"Rizzo",
"JR",
""
]
] |
TITLE: Multi-faceted Sensory Substitution for Curb Alerting: A Pilot
Investigation in Persons with Blindness and Low Vision
ABSTRACT: Curbs -- the edge of a raised sidewalk at the point where it meets a street
-- crucial in urban environments where they help delineate safe pedestrian
zones, from dangerous vehicular lanes. However, curbs themselves are
significant navigation hazards, particularly for people who are blind or have
low vision (pBLV). The challenges faced by pBLV in detecting and properly
orientating themselves for these abrupt elevation changes can lead to falls and
serious injuries. Despite recent advancements in assistive technologies, the
detection and early warning of curbs remains a largely unsolved challenge. This
paper aims to tackle this gap by introducing a novel, multi-faceted sensory
substitution approach hosted on a smart wearable; the platform leverages an RGB
camera and an embedded system to capture and segment curbs in real time and
provide early warning and orientation information. The system utilizes YOLO
(You Only Look Once) v8 segmentation model, trained on our custom curb dataset
for the camera input. The output of the system consists of adaptive auditory
beeps, abstract sonification, and speech, conveying information about the
relative distance and orientation of curbs. Through human-subjects
experimentation, we demonstrate the effectiveness of the system as compared to
the white cane. Results show that our system can provide advanced warning
through a larger safety window than the cane, while offering nearly identical
curb orientation information.
|
new_dataset
| 0.964355
|
2409.01790
|
Shiwen Ni
|
Shiwen Ni, Xiangtao Kong, Chengming Li, Xiping Hu, Ruifeng Xu, Jia
Zhu, Min Yang
|
Training on the Benchmark Is Not All You Need
| null |
AAAI 2025
| null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The success of Large Language Models (LLMs) relies heavily on the huge amount
of pre-training data learned in the pre-training phase. The opacity of the
pre-training process and the training data causes the results of many benchmark
tests to become unreliable. If any model has been trained on a benchmark test
set, it can seriously hinder the health of the field. In order to automate and
efficiently test the capabilities of large language models, numerous mainstream
benchmarks adopt a multiple-choice format. As the swapping of the contents of
multiple-choice options does not affect the meaning of the question itself, we
propose a simple and effective data leakage detection method based on this
property. Specifically, we shuffle the contents of the options in the data to
generate the corresponding derived data sets, and then detect data leakage
based on the model's log probability distribution over the derived data sets.
If there is a maximum and outlier in the set of log probabilities, it indicates
that the data is leaked. Our method is able to work under gray-box conditions
without access to model training data or weights, effectively identifying data
leakage from benchmark test sets in model pre-training data, including both
normal scenarios and complex scenarios where options may have been shuffled
intentionally or unintentionally. Through experiments based on two LLMs and
benchmark designs, we demonstrate the effectiveness of our method. In addition,
we evaluate the degree of data leakage of 35 mainstream open-source LLMs on
four benchmark datasets and give a ranking of the leaked LLMs for each
benchmark, and we find that the Qwen family of LLMs has the highest degree of
data leakage.
|
[
{
"version": "v1",
"created": "Tue, 3 Sep 2024 11:09:44 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 02:40:58 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Ni",
"Shiwen",
""
],
[
"Kong",
"Xiangtao",
""
],
[
"Li",
"Chengming",
""
],
[
"Hu",
"Xiping",
""
],
[
"Xu",
"Ruifeng",
""
],
[
"Zhu",
"Jia",
""
],
[
"Yang",
"Min",
""
]
] |
TITLE: Training on the Benchmark Is Not All You Need
ABSTRACT: The success of Large Language Models (LLMs) relies heavily on the huge amount
of pre-training data learned in the pre-training phase. The opacity of the
pre-training process and the training data causes the results of many benchmark
tests to become unreliable. If any model has been trained on a benchmark test
set, it can seriously hinder the health of the field. In order to automate and
efficiently test the capabilities of large language models, numerous mainstream
benchmarks adopt a multiple-choice format. As the swapping of the contents of
multiple-choice options does not affect the meaning of the question itself, we
propose a simple and effective data leakage detection method based on this
property. Specifically, we shuffle the contents of the options in the data to
generate the corresponding derived data sets, and then detect data leakage
based on the model's log probability distribution over the derived data sets.
If there is a maximum and outlier in the set of log probabilities, it indicates
that the data is leaked. Our method is able to work under gray-box conditions
without access to model training data or weights, effectively identifying data
leakage from benchmark test sets in model pre-training data, including both
normal scenarios and complex scenarios where options may have been shuffled
intentionally or unintentionally. Through experiments based on two LLMs and
benchmark designs, we demonstrate the effectiveness of our method. In addition,
we evaluate the degree of data leakage of 35 mainstream open-source LLMs on
four benchmark datasets and give a ranking of the leaked LLMs for each
benchmark, and we find that the Qwen family of LLMs has the highest degree of
data leakage.
|
no_new_dataset
| 0.94545
|
2409.02392
|
Wei Xiong
|
Wei Xiong, Chengshuai Shi, Jiaming Shen, Aviv Rosenberg, Zhen Qin,
Daniele Calandriello, Misha Khalman, Rishabh Joshi, Bilal Piot, Mohammad
Saleh, Chi Jin, Tong Zhang, Tianqi Liu
|
Building Math Agents with Multi-Turn Iterative Preference Learning
|
A multi-turn direct preference learning framework for tool-integrated
reasoning tasks
| null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent studies have shown that large language models' (LLMs) mathematical
problem-solving capabilities can be enhanced by integrating external tools,
such as code interpreters, and employing multi-turn Chain-of-Thought (CoT)
reasoning. While current methods focus on synthetic data generation and
Supervised Fine-Tuning (SFT), this paper studies the complementary direct
preference learning approach to further improve model performance. However,
existing direct preference learning algorithms are originally designed for the
single-turn chat task, and do not fully address the complexities of multi-turn
reasoning and external tool integration required for tool-integrated
mathematical reasoning tasks. To fill in this gap, we introduce a multi-turn
direct preference learning framework, tailored for this context, that leverages
feedback from code interpreters and optimizes trajectory-level preferences.
This framework includes multi-turn DPO and multi-turn KTO as specific
implementations. The effectiveness of our framework is validated through
training of various language models using an augmented prompt set from the
GSM8K and MATH datasets. Our results demonstrate substantial improvements: a
supervised fine-tuned Gemma-1.1-it-7B model's performance increased from 77.5%
to 83.9% on GSM8K and from 46.1% to 51.2% on MATH. Similarly, a Gemma-2-it-9B
model improved from 84.1% to 86.3% on GSM8K and from 51.0% to 54.5% on MATH.
|
[
{
"version": "v1",
"created": "Wed, 4 Sep 2024 02:41:04 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Feb 2025 22:10:16 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Xiong",
"Wei",
""
],
[
"Shi",
"Chengshuai",
""
],
[
"Shen",
"Jiaming",
""
],
[
"Rosenberg",
"Aviv",
""
],
[
"Qin",
"Zhen",
""
],
[
"Calandriello",
"Daniele",
""
],
[
"Khalman",
"Misha",
""
],
[
"Joshi",
"Rishabh",
""
],
[
"Piot",
"Bilal",
""
],
[
"Saleh",
"Mohammad",
""
],
[
"Jin",
"Chi",
""
],
[
"Zhang",
"Tong",
""
],
[
"Liu",
"Tianqi",
""
]
] |
TITLE: Building Math Agents with Multi-Turn Iterative Preference Learning
ABSTRACT: Recent studies have shown that large language models' (LLMs) mathematical
problem-solving capabilities can be enhanced by integrating external tools,
such as code interpreters, and employing multi-turn Chain-of-Thought (CoT)
reasoning. While current methods focus on synthetic data generation and
Supervised Fine-Tuning (SFT), this paper studies the complementary direct
preference learning approach to further improve model performance. However,
existing direct preference learning algorithms are originally designed for the
single-turn chat task, and do not fully address the complexities of multi-turn
reasoning and external tool integration required for tool-integrated
mathematical reasoning tasks. To fill in this gap, we introduce a multi-turn
direct preference learning framework, tailored for this context, that leverages
feedback from code interpreters and optimizes trajectory-level preferences.
This framework includes multi-turn DPO and multi-turn KTO as specific
implementations. The effectiveness of our framework is validated through
training of various language models using an augmented prompt set from the
GSM8K and MATH datasets. Our results demonstrate substantial improvements: a
supervised fine-tuned Gemma-1.1-it-7B model's performance increased from 77.5%
to 83.9% on GSM8K and from 46.1% to 51.2% on MATH. Similarly, a Gemma-2-it-9B
model improved from 84.1% to 86.3% on GSM8K and from 51.0% to 54.5% on MATH.
|
no_new_dataset
| 0.942454
|
2409.03550
|
Qianlong Xiang
|
Qianlong Xiang, Miao Zhang, Yuzhang Shang, Jianlong Wu, Yan Yan,
Liqiang Nie
|
DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any
Architecture
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Diffusion models (DMs) have demonstrated exceptional generative capabilities
across various domains, including image, video, and so on. A key factor
contributing to their effectiveness is the high quantity and quality of data
used during training. However, mainstream DMs now consume increasingly large
amounts of data. For example, training a Stable Diffusion model requires
billions of image-text pairs. This enormous data requirement poses significant
challenges for training large DMs due to high data acquisition costs and
storage expenses. To alleviate this data burden, we propose a novel scenario:
using existing DMs as data sources to train new DMs with any architecture. We
refer to this scenario as Data-Free Knowledge Distillation for Diffusion Models
(DKDM), where the generative ability of DMs is transferred to new ones in a
data-free manner. To tackle this challenge, we make two main contributions.
First, we introduce a DKDM objective that enables the training of new DMs via
distillation, without requiring access to the data. Second, we develop a
dynamic iterative distillation method that efficiently extracts time-domain
knowledge from existing DMs, enabling direct retrieval of training data without
the need for a prolonged generative process. To the best of our knowledge, we
are the first to explore this scenario. Experimental results demonstrate that
our data-free approach not only achieves competitive generative performance but
also, in some instances, outperforms models trained with the entire dataset.
|
[
{
"version": "v1",
"created": "Thu, 5 Sep 2024 14:12:22 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 15:26:03 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Xiang",
"Qianlong",
""
],
[
"Zhang",
"Miao",
""
],
[
"Shang",
"Yuzhang",
""
],
[
"Wu",
"Jianlong",
""
],
[
"Yan",
"Yan",
""
],
[
"Nie",
"Liqiang",
""
]
] |
TITLE: DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any
Architecture
ABSTRACT: Diffusion models (DMs) have demonstrated exceptional generative capabilities
across various domains, including image, video, and so on. A key factor
contributing to their effectiveness is the high quantity and quality of data
used during training. However, mainstream DMs now consume increasingly large
amounts of data. For example, training a Stable Diffusion model requires
billions of image-text pairs. This enormous data requirement poses significant
challenges for training large DMs due to high data acquisition costs and
storage expenses. To alleviate this data burden, we propose a novel scenario:
using existing DMs as data sources to train new DMs with any architecture. We
refer to this scenario as Data-Free Knowledge Distillation for Diffusion Models
(DKDM), where the generative ability of DMs is transferred to new ones in a
data-free manner. To tackle this challenge, we make two main contributions.
First, we introduce a DKDM objective that enables the training of new DMs via
distillation, without requiring access to the data. Second, we develop a
dynamic iterative distillation method that efficiently extracts time-domain
knowledge from existing DMs, enabling direct retrieval of training data without
the need for a prolonged generative process. To the best of our knowledge, we
are the first to explore this scenario. Experimental results demonstrate that
our data-free approach not only achieves competitive generative performance but
also, in some instances, outperforms models trained with the entire dataset.
|
no_new_dataset
| 0.945751
|
2409.10653
|
Raika Karimi
|
Raika Karimi, Faezeh Faez, Yingxue Zhang, Xing Li, Lei Chen, Mingxuan
Yuan, Mahdi Biparva
|
Logic Synthesis Optimization with Predictive Self-Supervision via Causal
Transformers
| null | null | null | null |
cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Contemporary hardware design benefits from the abstraction provided by
high-level logic gates, streamlining the implementation of logic circuits.
Logic Synthesis Optimization (LSO) operates at one level of abstraction within
the Electronic Design Automation (EDA) workflow, targeting improvements in
logic circuits with respect to performance metrics such as size and speed in
the final layout. Recent trends in the field show a growing interest in
leveraging Machine Learning (ML) for EDA, notably through ML-guided logic
synthesis utilizing policy-based Reinforcement Learning (RL) methods.Despite
these advancements, existing models face challenges such as overfitting and
limited generalization, attributed to constrained public circuits and the
expressiveness limitations of graph encoders. To address these hurdles, and
tackle data scarcity issues, we introduce LSOformer, a novel approach
harnessing Autoregressive transformer models and predictive SSL to predict the
trajectory of Quality of Results (QoR). LSOformer integrates cross-attention
modules to merge insights from circuit graphs and optimization sequences,
thereby enhancing prediction accuracy for QoR metrics. Experimental studies
validate the effectiveness of LSOformer, showcasing its superior performance
over baseline architectures in QoR prediction tasks, where it achieves
improvements of 5.74%, 4.35%, and 17.06% on the EPFL, OABCD, and proprietary
circuits datasets, respectively, in inductive setup.
|
[
{
"version": "v1",
"created": "Mon, 16 Sep 2024 18:45:07 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 16:04:54 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Karimi",
"Raika",
""
],
[
"Faez",
"Faezeh",
""
],
[
"Zhang",
"Yingxue",
""
],
[
"Li",
"Xing",
""
],
[
"Chen",
"Lei",
""
],
[
"Yuan",
"Mingxuan",
""
],
[
"Biparva",
"Mahdi",
""
]
] |
TITLE: Logic Synthesis Optimization with Predictive Self-Supervision via Causal
Transformers
ABSTRACT: Contemporary hardware design benefits from the abstraction provided by
high-level logic gates, streamlining the implementation of logic circuits.
Logic Synthesis Optimization (LSO) operates at one level of abstraction within
the Electronic Design Automation (EDA) workflow, targeting improvements in
logic circuits with respect to performance metrics such as size and speed in
the final layout. Recent trends in the field show a growing interest in
leveraging Machine Learning (ML) for EDA, notably through ML-guided logic
synthesis utilizing policy-based Reinforcement Learning (RL) methods.Despite
these advancements, existing models face challenges such as overfitting and
limited generalization, attributed to constrained public circuits and the
expressiveness limitations of graph encoders. To address these hurdles, and
tackle data scarcity issues, we introduce LSOformer, a novel approach
harnessing Autoregressive transformer models and predictive SSL to predict the
trajectory of Quality of Results (QoR). LSOformer integrates cross-attention
modules to merge insights from circuit graphs and optimization sequences,
thereby enhancing prediction accuracy for QoR metrics. Experimental studies
validate the effectiveness of LSOformer, showcasing its superior performance
over baseline architectures in QoR prediction tasks, where it achieves
improvements of 5.74%, 4.35%, and 17.06% on the EPFL, OABCD, and proprietary
circuits datasets, respectively, in inductive setup.
|
no_new_dataset
| 0.943243
|
2409.16238
|
Dominic Phillips
|
Jonathan Feldstein, Dominic Phillips, Efthymia Tsamoura
|
Efficiently Learning Probabilistic Logical Models by Cheaply Ranking
Mined Rules
|
21 pages
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Probabilistic logical models are a core component of neurosymbolic AI and are
important in their own right for tasks that require high explainability. Unlike
neural networks, logical theories that underlie the model are often handcrafted
using domain expertise, making their development costly and prone to errors.
While there are algorithms that learn logical theories from data, they are
generally prohibitively expensive, limiting their applicability in real-world
settings. Here, we introduce precision and recall for logical rules and define
their composition as rule utility -- a cost-effective measure of the predictive
power of logical theories. We also introduce SPECTRUM, a scalable framework for
learning logical theories from relational data. Its scalability derives from a
linear-time algorithm that mines recurrent subgraphs in the data graph along
with a second algorithm that, using the cheap utility measure, efficiently
ranks rules derived from these subgraphs. Finally, we prove theoretical
guarantees on the utility of the learnt logical theory. As a result, we
demonstrate across various tasks that SPECTRUM scales to larger datasets, often
learning more accurate logical theories on CPUs in < 1% the runtime of SOTA
neural network approaches on GPUs.
|
[
{
"version": "v1",
"created": "Tue, 24 Sep 2024 16:54:12 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 16:29:51 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Feldstein",
"Jonathan",
""
],
[
"Phillips",
"Dominic",
""
],
[
"Tsamoura",
"Efthymia",
""
]
] |
TITLE: Efficiently Learning Probabilistic Logical Models by Cheaply Ranking
Mined Rules
ABSTRACT: Probabilistic logical models are a core component of neurosymbolic AI and are
important in their own right for tasks that require high explainability. Unlike
neural networks, logical theories that underlie the model are often handcrafted
using domain expertise, making their development costly and prone to errors.
While there are algorithms that learn logical theories from data, they are
generally prohibitively expensive, limiting their applicability in real-world
settings. Here, we introduce precision and recall for logical rules and define
their composition as rule utility -- a cost-effective measure of the predictive
power of logical theories. We also introduce SPECTRUM, a scalable framework for
learning logical theories from relational data. Its scalability derives from a
linear-time algorithm that mines recurrent subgraphs in the data graph along
with a second algorithm that, using the cheap utility measure, efficiently
ranks rules derived from these subgraphs. Finally, we prove theoretical
guarantees on the utility of the learnt logical theory. As a result, we
demonstrate across various tasks that SPECTRUM scales to larger datasets, often
learning more accurate logical theories on CPUs in < 1% the runtime of SOTA
neural network approaches on GPUs.
|
no_new_dataset
| 0.948202
|
2410.00477
|
Pranav Gupta
|
Pranav Gupta, Advith Krishnan, Naman Nanda, Ananth Eswar, Deeksha
Agarwal, Pratham Gohil, Pratyush Goel
|
ViDAS: Vision-based Danger Assessment and Scoring
|
Preprint
| null |
10.1145/3702250.3702279
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We present a novel dataset aimed at advancing danger analysis and assessment
by addressing the challenge of quantifying danger in video content and
identifying how human-like a Large Language Model (LLM) evaluator is for the
same. This is achieved by compiling a collection of 100 YouTube videos
featuring various events. Each video is annotated by human participants who
provided danger ratings on a scale from 0 (no danger to humans) to 10
(life-threatening), with precise timestamps indicating moments of heightened
danger. Additionally, we leverage LLMs to independently assess the danger
levels in these videos using video summaries. We introduce Mean Squared Error
(MSE) scores for multimodal meta-evaluation of the alignment between human and
LLM danger assessments. Our dataset not only contributes a new resource for
danger assessment in video content but also demonstrates the potential of LLMs
in achieving human-like evaluations.
|
[
{
"version": "v1",
"created": "Tue, 1 Oct 2024 08:06:46 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Gupta",
"Pranav",
""
],
[
"Krishnan",
"Advith",
""
],
[
"Nanda",
"Naman",
""
],
[
"Eswar",
"Ananth",
""
],
[
"Agarwal",
"Deeksha",
""
],
[
"Gohil",
"Pratham",
""
],
[
"Goel",
"Pratyush",
""
]
] |
TITLE: ViDAS: Vision-based Danger Assessment and Scoring
ABSTRACT: We present a novel dataset aimed at advancing danger analysis and assessment
by addressing the challenge of quantifying danger in video content and
identifying how human-like a Large Language Model (LLM) evaluator is for the
same. This is achieved by compiling a collection of 100 YouTube videos
featuring various events. Each video is annotated by human participants who
provided danger ratings on a scale from 0 (no danger to humans) to 10
(life-threatening), with precise timestamps indicating moments of heightened
danger. Additionally, we leverage LLMs to independently assess the danger
levels in these videos using video summaries. We introduce Mean Squared Error
(MSE) scores for multimodal meta-evaluation of the alignment between human and
LLM danger assessments. Our dataset not only contributes a new resource for
danger assessment in video content but also demonstrates the potential of LLMs
in achieving human-like evaluations.
|
new_dataset
| 0.955402
|
2410.01628
|
Aron Distelzweig
|
Aron Distelzweig, Andreas Look, Eitan Kosman, Faris Janjo\v{s}, J\"org
Wagner, Abhinav Valada
|
Stochasticity in Motion: An Information-Theoretic Approach to Trajectory
Prediction
|
8 pages, 5 figures, submitted to International Conference on
Intelligent Robots and Systems (IROS 2025)
| null | null | null |
cs.RO cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In autonomous driving, accurate motion prediction is crucial for safe and
efficient motion planning. To ensure safety, planners require reliable
uncertainty estimates of the predicted behavior of surrounding agents, yet this
aspect has received limited attention. In particular, decomposing uncertainty
into its aleatoric and epistemic components is essential for distinguishing
between inherent environmental randomness and model uncertainty, thereby
enabling more robust and informed decision-making. This paper addresses the
challenge of uncertainty modeling in trajectory prediction with a holistic
approach that emphasizes uncertainty quantification, decomposition, and the
impact of model composition. Our method, grounded in information theory,
provides a theoretically principled way to measure uncertainty and decompose it
into aleatoric and epistemic components. Unlike prior work, our approach is
compatible with state-of-the-art motion predictors, allowing for broader
applicability. We demonstrate its utility by conducting extensive experiments
on the nuScenes dataset, which shows how different architectures and
configurations influence uncertainty quantification and model robustness.
|
[
{
"version": "v1",
"created": "Wed, 2 Oct 2024 15:02:32 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Oct 2024 11:57:37 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 16:28:50 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Distelzweig",
"Aron",
""
],
[
"Look",
"Andreas",
""
],
[
"Kosman",
"Eitan",
""
],
[
"Janjoš",
"Faris",
""
],
[
"Wagner",
"Jörg",
""
],
[
"Valada",
"Abhinav",
""
]
] |
TITLE: Stochasticity in Motion: An Information-Theoretic Approach to Trajectory
Prediction
ABSTRACT: In autonomous driving, accurate motion prediction is crucial for safe and
efficient motion planning. To ensure safety, planners require reliable
uncertainty estimates of the predicted behavior of surrounding agents, yet this
aspect has received limited attention. In particular, decomposing uncertainty
into its aleatoric and epistemic components is essential for distinguishing
between inherent environmental randomness and model uncertainty, thereby
enabling more robust and informed decision-making. This paper addresses the
challenge of uncertainty modeling in trajectory prediction with a holistic
approach that emphasizes uncertainty quantification, decomposition, and the
impact of model composition. Our method, grounded in information theory,
provides a theoretically principled way to measure uncertainty and decompose it
into aleatoric and epistemic components. Unlike prior work, our approach is
compatible with state-of-the-art motion predictors, allowing for broader
applicability. We demonstrate its utility by conducting extensive experiments
on the nuScenes dataset, which shows how different architectures and
configurations influence uncertainty quantification and model robustness.
|
no_new_dataset
| 0.940188
|
2410.01671
|
Yanming Liu
|
Yanming Liu, Xinyue Peng, Jiannan Cao, Shi Bo, Yanxin Shen, Tianyu Du,
Sheng Cheng, Xun Wang, Jianwei Yin, Xuhong Zhang
|
Bridging Context Gaps: Leveraging Coreference Resolution for Long
Contextual Understanding
|
ICLR 2025 camera ready version, with updated metadata
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Large language models (LLMs) have shown remarkable capabilities in natural
language processing; however, they still face difficulties when tasked with
understanding lengthy contexts and executing effective question answering.
These challenges often arise due to the complexity and ambiguity present in
longer texts. To enhance the performance of LLMs in such scenarios, we
introduce the Long Question Coreference Adaptation (LQCA) method. This
innovative framework focuses on coreference resolution tailored to long
contexts, allowing the model to identify and manage references effectively. The
LQCA method encompasses four key steps: resolving coreferences within
sub-documents, computing the distances between mentions, defining a
representative mention for coreference, and answering questions through mention
replacement. By processing information systematically, the framework provides
easier-to-handle partitions for LLMs, promoting better understanding.
Experimental evaluations on a range of LLMs and datasets have yielded positive
results, with a notable improvements on OpenAI-o1-mini and GPT-4o models,
highlighting the effectiveness of leveraging coreference resolution to bridge
context gaps in question answering. Our code is public at
https://github.com/OceannTwT/LQCA.
|
[
{
"version": "v1",
"created": "Wed, 2 Oct 2024 15:39:55 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 07:09:00 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Liu",
"Yanming",
""
],
[
"Peng",
"Xinyue",
""
],
[
"Cao",
"Jiannan",
""
],
[
"Bo",
"Shi",
""
],
[
"Shen",
"Yanxin",
""
],
[
"Du",
"Tianyu",
""
],
[
"Cheng",
"Sheng",
""
],
[
"Wang",
"Xun",
""
],
[
"Yin",
"Jianwei",
""
],
[
"Zhang",
"Xuhong",
""
]
] |
TITLE: Bridging Context Gaps: Leveraging Coreference Resolution for Long
Contextual Understanding
ABSTRACT: Large language models (LLMs) have shown remarkable capabilities in natural
language processing; however, they still face difficulties when tasked with
understanding lengthy contexts and executing effective question answering.
These challenges often arise due to the complexity and ambiguity present in
longer texts. To enhance the performance of LLMs in such scenarios, we
introduce the Long Question Coreference Adaptation (LQCA) method. This
innovative framework focuses on coreference resolution tailored to long
contexts, allowing the model to identify and manage references effectively. The
LQCA method encompasses four key steps: resolving coreferences within
sub-documents, computing the distances between mentions, defining a
representative mention for coreference, and answering questions through mention
replacement. By processing information systematically, the framework provides
easier-to-handle partitions for LLMs, promoting better understanding.
Experimental evaluations on a range of LLMs and datasets have yielded positive
results, with a notable improvements on OpenAI-o1-mini and GPT-4o models,
highlighting the effectiveness of leveraging coreference resolution to bridge
context gaps in question answering. Our code is public at
https://github.com/OceannTwT/LQCA.
|
no_new_dataset
| 0.942507
|
2410.01767
|
Carlos Miguel Pati\~no
|
Santiago Cortes-Gomez, Carlos Pati\~no, Yewon Byun, Steven Wu, Eric
Horvitz, Bryan Wilder
|
Utility-Directed Conformal Prediction: A Decision-Aware Framework for
Actionable Uncertainty Quantification
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Interest has been growing in decision-focused machine learning methods which
train models to account for how their predictions are used in downstream
optimization problems. Doing so can often improve performance on subsequent
decision problems. However, current methods for uncertainty quantification do
not incorporate any information about downstream decisions. We develop a
methodology based on conformal prediction to identify prediction sets that
account for a downstream cost function, making them more appropriate to inform
high-stakes decision-making. Our approach harnesses the strengths of conformal
methods -- modularity, model-agnosticism, and statistical coverage guarantees
-- while incorporating downstream decisions and user-specified utility
functions. We prove that our methods retain standard coverage guarantees.
Empirical evaluation across a range of datasets and utility metrics
demonstrates that our methods achieve significantly lower costs than standard
conformal methods. We present a real-world use case in healthcare diagnosis,
where our method effectively incorporates the hierarchical structure of
dermatological diseases. The method successfully generates sets with coherent
diagnostic meaning, potentially aiding triage for dermatology diagnosis and
illustrating how our method can ground high-stakes decision-making employing
domain knowledge.
|
[
{
"version": "v1",
"created": "Wed, 2 Oct 2024 17:22:09 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 09:26:15 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Cortes-Gomez",
"Santiago",
""
],
[
"Patiño",
"Carlos",
""
],
[
"Byun",
"Yewon",
""
],
[
"Wu",
"Steven",
""
],
[
"Horvitz",
"Eric",
""
],
[
"Wilder",
"Bryan",
""
]
] |
TITLE: Utility-Directed Conformal Prediction: A Decision-Aware Framework for
Actionable Uncertainty Quantification
ABSTRACT: Interest has been growing in decision-focused machine learning methods which
train models to account for how their predictions are used in downstream
optimization problems. Doing so can often improve performance on subsequent
decision problems. However, current methods for uncertainty quantification do
not incorporate any information about downstream decisions. We develop a
methodology based on conformal prediction to identify prediction sets that
account for a downstream cost function, making them more appropriate to inform
high-stakes decision-making. Our approach harnesses the strengths of conformal
methods -- modularity, model-agnosticism, and statistical coverage guarantees
-- while incorporating downstream decisions and user-specified utility
functions. We prove that our methods retain standard coverage guarantees.
Empirical evaluation across a range of datasets and utility metrics
demonstrates that our methods achieve significantly lower costs than standard
conformal methods. We present a real-world use case in healthcare diagnosis,
where our method effectively incorporates the hierarchical structure of
dermatological diseases. The method successfully generates sets with coherent
diagnostic meaning, potentially aiding triage for dermatology diagnosis and
illustrating how our method can ground high-stakes decision-making employing
domain knowledge.
|
no_new_dataset
| 0.945901
|
2410.03074
|
Yuehan Qin
|
Yuehan Qin, Yichi Zhang, Yi Nian, Xueying Ding, Yue Zhao
|
MetaOOD: Automatic Selection of OOD Detection Models
|
Best paper at 2024 KDD Workshop on Resource-Efficient Learning.
Extended version at ICLR 2025
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
How can we automatically select an out-of-distribution (OOD) detection model
for various underlying tasks? This is crucial for maintaining the reliability
of open-world applications by identifying data distribution shifts,
particularly in critical domains such as online transactions, autonomous
driving, and real-time patient diagnosis. Despite the availability of numerous
OOD detection methods, the challenge of selecting an optimal model for diverse
tasks remains largely underexplored, especially in scenarios lacking ground
truth labels. In this work, we introduce MetaOOD, the first zero-shot,
unsupervised framework that utilizes meta-learning to select an OOD detection
model automatically. As a meta-learning approach, MetaOOD leverages historical
performance data of existing methods across various benchmark OOD detection
datasets, enabling the effective selection of a suitable model for new datasets
without the need for labeled data at the test time. To quantify task
similarities more accurately, we introduce language model-based embeddings that
capture the distinctive OOD characteristics of both datasets and detection
models. Through extensive experimentation with 24 unique test dataset pairs to
choose from among 11 OOD detection models, we demonstrate that MetaOOD
significantly outperforms existing methods and only brings marginal time
overhead. Our results, validated by Wilcoxon statistical tests, show that
MetaOOD surpasses a diverse group of 11 baselines, including established OOD
detectors and advanced unsupervised selection methods.
|
[
{
"version": "v1",
"created": "Fri, 4 Oct 2024 01:36:19 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 05:14:32 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Qin",
"Yuehan",
""
],
[
"Zhang",
"Yichi",
""
],
[
"Nian",
"Yi",
""
],
[
"Ding",
"Xueying",
""
],
[
"Zhao",
"Yue",
""
]
] |
TITLE: MetaOOD: Automatic Selection of OOD Detection Models
ABSTRACT: How can we automatically select an out-of-distribution (OOD) detection model
for various underlying tasks? This is crucial for maintaining the reliability
of open-world applications by identifying data distribution shifts,
particularly in critical domains such as online transactions, autonomous
driving, and real-time patient diagnosis. Despite the availability of numerous
OOD detection methods, the challenge of selecting an optimal model for diverse
tasks remains largely underexplored, especially in scenarios lacking ground
truth labels. In this work, we introduce MetaOOD, the first zero-shot,
unsupervised framework that utilizes meta-learning to select an OOD detection
model automatically. As a meta-learning approach, MetaOOD leverages historical
performance data of existing methods across various benchmark OOD detection
datasets, enabling the effective selection of a suitable model for new datasets
without the need for labeled data at the test time. To quantify task
similarities more accurately, we introduce language model-based embeddings that
capture the distinctive OOD characteristics of both datasets and detection
models. Through extensive experimentation with 24 unique test dataset pairs to
choose from among 11 OOD detection models, we demonstrate that MetaOOD
significantly outperforms existing methods and only brings marginal time
overhead. Our results, validated by Wilcoxon statistical tests, show that
MetaOOD surpasses a diverse group of 11 baselines, including established OOD
detectors and advanced unsupervised selection methods.
|
no_new_dataset
| 0.942929
|
2410.05602
|
Byoungwoo Park
|
Byoungwoo Park, Hyungi Lee, Juho Lee
|
Amortized Control of Continuous State Space Feynman-Kac Model for
Irregular Time Series
| null | null | null | null |
stat.ML cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Many real-world datasets, such as healthcare, climate, and economics, are
often collected as irregular time series, which poses challenges for accurate
modeling. In this paper, we propose the Amortized Control of continuous State
Space Model (ACSSM) for continuous dynamical modeling of time series for
irregular and discrete observations. We first present a multi-marginal Doob's
$h$-transform to construct a continuous dynamical system conditioned on these
irregular observations. Following this, we introduce a variational inference
algorithm with a tight evidence lower bound (ELBO), leveraging stochastic
optimal control (SOC) theory to approximate the intractable Doob's
$h$-transform and simulate the conditioned dynamics. To improve efficiency and
scalability during both training and inference, ACSSM leverages auxiliary
variable to flexibly parameterize the latent dynamics and amortized control.
Additionally, it incorporates a simulation-free latent dynamics framework and a
transformer-based data assimilation scheme, facilitating parallel inference of
the latent states and ELBO computation. Through empirical evaluations across a
variety of real-world datasets, ACSSM demonstrates superior performance in
tasks such as classification, regression, interpolation, and extrapolation,
while maintaining computational efficiency.
|
[
{
"version": "v1",
"created": "Tue, 8 Oct 2024 01:27:46 GMT"
},
{
"version": "v2",
"created": "Tue, 25 Feb 2025 00:18:24 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 03:30:08 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Park",
"Byoungwoo",
""
],
[
"Lee",
"Hyungi",
""
],
[
"Lee",
"Juho",
""
]
] |
TITLE: Amortized Control of Continuous State Space Feynman-Kac Model for
Irregular Time Series
ABSTRACT: Many real-world datasets, such as healthcare, climate, and economics, are
often collected as irregular time series, which poses challenges for accurate
modeling. In this paper, we propose the Amortized Control of continuous State
Space Model (ACSSM) for continuous dynamical modeling of time series for
irregular and discrete observations. We first present a multi-marginal Doob's
$h$-transform to construct a continuous dynamical system conditioned on these
irregular observations. Following this, we introduce a variational inference
algorithm with a tight evidence lower bound (ELBO), leveraging stochastic
optimal control (SOC) theory to approximate the intractable Doob's
$h$-transform and simulate the conditioned dynamics. To improve efficiency and
scalability during both training and inference, ACSSM leverages auxiliary
variable to flexibly parameterize the latent dynamics and amortized control.
Additionally, it incorporates a simulation-free latent dynamics framework and a
transformer-based data assimilation scheme, facilitating parallel inference of
the latent states and ELBO computation. Through empirical evaluations across a
variety of real-world datasets, ACSSM demonstrates superior performance in
tasks such as classification, regression, interpolation, and extrapolation,
while maintaining computational efficiency.
|
no_new_dataset
| 0.948394
|
2410.08014
|
Kai Zhang
|
Kai Zhang, Congchao Wang, Liqian Peng, Alec Go, Xiaozhong Liu
|
Privacy-preserved LLM Cascade via CoT-enhanced Policy Learning
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Large Language Models (LLMs) have gained significant attention in on-device
applications due to their remarkable performance across real-world tasks.
However, on-device LLMs often suffer from suboptimal performance due to
hardware limitations. A promising solution to this challenge is cascading a
weaker local (on-device) LLM with a more powerful server LLM. While existing
research on LLM cascade primarily optimizes the performance-cost trade-off,
real-world applications impose additional requirements, such as privacy
preservation, which remain largely unaddressed. In this work, we move beyond
existing confidence- and logit-based LLM cascade methods and propose
$\mathbf{P^{3}Defer}$, a novel Chain-of-Thought (CoT)-enhanced \textbf{p}olicy
learning framework for \textbf{p}rivacy-\textbf{p}reserved \textbf{defer}ral
decision-making. Our approach effectively improves cascade efficiency while
mitigating privacy risks. Extensive experiments on three benchmark datasets
demonstrate the effectiveness and superiority of $\mathbf{P^{3}Defer}$ over
existing methods.
|
[
{
"version": "v1",
"created": "Thu, 10 Oct 2024 15:09:52 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Feb 2025 17:56:08 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Zhang",
"Kai",
""
],
[
"Wang",
"Congchao",
""
],
[
"Peng",
"Liqian",
""
],
[
"Go",
"Alec",
""
],
[
"Liu",
"Xiaozhong",
""
]
] |
TITLE: Privacy-preserved LLM Cascade via CoT-enhanced Policy Learning
ABSTRACT: Large Language Models (LLMs) have gained significant attention in on-device
applications due to their remarkable performance across real-world tasks.
However, on-device LLMs often suffer from suboptimal performance due to
hardware limitations. A promising solution to this challenge is cascading a
weaker local (on-device) LLM with a more powerful server LLM. While existing
research on LLM cascade primarily optimizes the performance-cost trade-off,
real-world applications impose additional requirements, such as privacy
preservation, which remain largely unaddressed. In this work, we move beyond
existing confidence- and logit-based LLM cascade methods and propose
$\mathbf{P^{3}Defer}$, a novel Chain-of-Thought (CoT)-enhanced \textbf{p}olicy
learning framework for \textbf{p}rivacy-\textbf{p}reserved \textbf{defer}ral
decision-making. Our approach effectively improves cascade efficiency while
mitigating privacy risks. Extensive experiments on three benchmark datasets
demonstrate the effectiveness and superiority of $\mathbf{P^{3}Defer}$ over
existing methods.
|
no_new_dataset
| 0.947769
|
2410.08388
|
Maximus Powers
|
Maximus Powers, Shaina Raza, Alex Chang, Umang Mavani, Harshitha Reddy
Jonala, Ansh Tiwari, Hua Wei
|
The GUS Framework: Benchmarking Social Bias Classification with
Discriminative (Encoder-Only) and Generative (Decoder-Only) Language Models
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
The detection of social bias in text is a critical challenge, particularly
due to the limitations of binary classification methods. These methods often
oversimplify nuanced biases, leading to high emotional impact when content is
misclassified as either "biased" or "fair." To address these shortcomings, we
propose a more nuanced framework that focuses on three key linguistic
components underlying social bias: Generalizations, Unfairness, and Stereotypes
(the GUS framework). The GUS framework employs a semi-automated approach to
create a comprehensive synthetic dataset, which is then verified by humans to
maintain ethical standards. This dataset enables robust multi-label token
classification. Our methodology, which combines discriminative (encoder-only)
models and generative (auto-regressive large language models), identifies
biased entities in text. Through extensive experiments, we demonstrate that
encoder-only models are effective for this complex task, often outperforming
state-of-the-art methods, both in terms of macro and entity-wise F1-score and
Hamming loss. These findings can guide the choice of model for different use
cases, highlighting the GUS framework's effectiveness in capturing explicit and
implicit biases across diverse contexts, and offering a pathway for future
research and applications in various fields.
|
[
{
"version": "v1",
"created": "Thu, 10 Oct 2024 21:51:22 GMT"
},
{
"version": "v2",
"created": "Thu, 17 Oct 2024 20:33:28 GMT"
},
{
"version": "v3",
"created": "Sun, 23 Feb 2025 17:08:56 GMT"
},
{
"version": "v4",
"created": "Fri, 28 Feb 2025 18:55:08 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Powers",
"Maximus",
""
],
[
"Raza",
"Shaina",
""
],
[
"Chang",
"Alex",
""
],
[
"Mavani",
"Umang",
""
],
[
"Jonala",
"Harshitha Reddy",
""
],
[
"Tiwari",
"Ansh",
""
],
[
"Wei",
"Hua",
""
]
] |
TITLE: The GUS Framework: Benchmarking Social Bias Classification with
Discriminative (Encoder-Only) and Generative (Decoder-Only) Language Models
ABSTRACT: The detection of social bias in text is a critical challenge, particularly
due to the limitations of binary classification methods. These methods often
oversimplify nuanced biases, leading to high emotional impact when content is
misclassified as either "biased" or "fair." To address these shortcomings, we
propose a more nuanced framework that focuses on three key linguistic
components underlying social bias: Generalizations, Unfairness, and Stereotypes
(the GUS framework). The GUS framework employs a semi-automated approach to
create a comprehensive synthetic dataset, which is then verified by humans to
maintain ethical standards. This dataset enables robust multi-label token
classification. Our methodology, which combines discriminative (encoder-only)
models and generative (auto-regressive large language models), identifies
biased entities in text. Through extensive experiments, we demonstrate that
encoder-only models are effective for this complex task, often outperforming
state-of-the-art methods, both in terms of macro and entity-wise F1-score and
Hamming loss. These findings can guide the choice of model for different use
cases, highlighting the GUS framework's effectiveness in capturing explicit and
implicit biases across diverse contexts, and offering a pathway for future
research and applications in various fields.
|
new_dataset
| 0.955817
|
2410.09542
|
Jiachun Li
|
Jiachun Li, Pengfei Cao, Zhuoran Jin, Yubo Chen, Kang Liu, Jun Zhao
|
MIRAGE: Evaluating and Explaining Inductive Reasoning Process in
Language Models
|
Accepted as ICLR 2025 conference paper (26 pages, 16 tables, 9
figures)
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Inductive reasoning is an essential capability for large language models
(LLMs) to achieve higher intelligence, which requires the model to generalize
rules from observed facts and then apply them to unseen examples. We present
MIRAGE, a synthetic dataset that addresses the limitations of previous work,
specifically the lack of comprehensive evaluation and flexible test data. In
it, we evaluate LLMs' capabilities in both the inductive and deductive stages,
allowing for flexible variation in input distribution, task scenario, and task
difficulty to analyze the factors influencing LLMs' inductive reasoning. Based
on these multi-faceted evaluations, we demonstrate that the LLM is a poor
rule-based reasoner. In many cases, when conducting inductive reasoning, they
do not rely on a correct rule to answer the unseen case. From the perspectives
of different prompting methods, observation numbers, and task forms, models
tend to consistently conduct correct deduction without correct inductive rules.
Besides, we find that LLMs are good neighbor-based reasoners. In the inductive
reasoning process, the model tends to focus on observed facts that are close to
the current test example in feature space. By leveraging these similar
examples, the model maintains strong inductive capabilities within a localized
region, significantly improving its deductive performance.
|
[
{
"version": "v1",
"created": "Sat, 12 Oct 2024 14:12:36 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 08:01:32 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Li",
"Jiachun",
""
],
[
"Cao",
"Pengfei",
""
],
[
"Jin",
"Zhuoran",
""
],
[
"Chen",
"Yubo",
""
],
[
"Liu",
"Kang",
""
],
[
"Zhao",
"Jun",
""
]
] |
TITLE: MIRAGE: Evaluating and Explaining Inductive Reasoning Process in
Language Models
ABSTRACT: Inductive reasoning is an essential capability for large language models
(LLMs) to achieve higher intelligence, which requires the model to generalize
rules from observed facts and then apply them to unseen examples. We present
MIRAGE, a synthetic dataset that addresses the limitations of previous work,
specifically the lack of comprehensive evaluation and flexible test data. In
it, we evaluate LLMs' capabilities in both the inductive and deductive stages,
allowing for flexible variation in input distribution, task scenario, and task
difficulty to analyze the factors influencing LLMs' inductive reasoning. Based
on these multi-faceted evaluations, we demonstrate that the LLM is a poor
rule-based reasoner. In many cases, when conducting inductive reasoning, they
do not rely on a correct rule to answer the unseen case. From the perspectives
of different prompting methods, observation numbers, and task forms, models
tend to consistently conduct correct deduction without correct inductive rules.
Besides, we find that LLMs are good neighbor-based reasoners. In the inductive
reasoning process, the model tends to focus on observed facts that are close to
the current test example in feature space. By leveraging these similar
examples, the model maintains strong inductive capabilities within a localized
region, significantly improving its deductive performance.
|
new_dataset
| 0.956675
|
2410.09570
|
Dingyi Zhuang
|
Dingyi Zhuang, Chonghe Jiang, Yunhan Zheng, Shenhao Wang, Jinhua Zhao
|
GETS: Ensemble Temperature Scaling for Calibration in Graph Neural
Networks
|
ICLR 2025 Spotlight
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Graph Neural Networks deliver strong classification results but often suffer
from poor calibration performance, leading to overconfidence or
underconfidence. This is particularly problematic in high stakes applications
where accurate uncertainty estimates are essential. Existing post hoc methods,
such as temperature scaling, fail to effectively utilize graph structures,
while current GNN calibration methods often overlook the potential of
leveraging diverse input information and model ensembles jointly. In the paper,
we propose Graph Ensemble Temperature Scaling, a novel calibration framework
that combines input and model ensemble strategies within a Graph Mixture of
Experts archi SOTA calibration techniques, reducing expected calibration error
by 25 percent across 10 GNN benchmark datasets. Additionally, GETS is
computationally efficient, scalable, and capable of selecting effective input
combinations for improved calibration performance. The implementation is
available via Github.
|
[
{
"version": "v1",
"created": "Sat, 12 Oct 2024 15:34:41 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Feb 2025 23:10:46 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Zhuang",
"Dingyi",
""
],
[
"Jiang",
"Chonghe",
""
],
[
"Zheng",
"Yunhan",
""
],
[
"Wang",
"Shenhao",
""
],
[
"Zhao",
"Jinhua",
""
]
] |
TITLE: GETS: Ensemble Temperature Scaling for Calibration in Graph Neural
Networks
ABSTRACT: Graph Neural Networks deliver strong classification results but often suffer
from poor calibration performance, leading to overconfidence or
underconfidence. This is particularly problematic in high stakes applications
where accurate uncertainty estimates are essential. Existing post hoc methods,
such as temperature scaling, fail to effectively utilize graph structures,
while current GNN calibration methods often overlook the potential of
leveraging diverse input information and model ensembles jointly. In the paper,
we propose Graph Ensemble Temperature Scaling, a novel calibration framework
that combines input and model ensemble strategies within a Graph Mixture of
Experts archi SOTA calibration techniques, reducing expected calibration error
by 25 percent across 10 GNN benchmark datasets. Additionally, GETS is
computationally efficient, scalable, and capable of selecting effective input
combinations for improved calibration performance. The implementation is
available via Github.
|
no_new_dataset
| 0.947769
|
2410.09870
|
Yein Park
|
Yein Park, Chanwoong Yoon, Jungwoo Park, Donghyeon Lee, Minbyul Jeong,
Jaewoo Kang
|
ChroKnowledge: Unveiling Chronological Knowledge of Language Models in
Multiple Domains
|
ICLR 2025, 40 pages, 17 figures
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Large language models (LLMs) have brought significant changes to many aspects
of our lives. However, assessing and ensuring their chronological knowledge
remains challenging. Existing approaches fall short in addressing the temporal
adaptability of knowledge, often relying on a fixed time-point view. To
overcome this, we introduce ChroKnowBench, a benchmark dataset designed to
evaluate chronologically accumulated knowledge across three key aspects:
multiple domains, time dependency, temporal state. Our benchmark distinguishes
between knowledge that evolves (e.g., personal history, scientific discoveries,
amended laws) and knowledge that remain constant (e.g., mathematical truths,
commonsense facts). Building on this benchmark, we present ChroKnowledge
(Chronological Categorization of Knowledge), a novel sampling-based framework
for evaluating LLMs' non-parametric chronological knowledge. Our evaluation led
to the following observations: (1) The ability of eliciting temporal knowledge
varies depending on the data format that model was trained on. (2) LLMs
partially recall knowledge or show a cut-off at temporal boundaries rather than
recalling all aspects of knowledge correctly. Thus, we apply our
ChroKnowPrompt, an in-depth prompting to elicit chronological knowledge by
traversing step-by-step through the surrounding time spans. We observe that it
successfully recalls objects across both open-source and proprietary LLMs,
demonstrating versatility, though it faces challenges with dynamic datasets and
unstructured formats.
|
[
{
"version": "v1",
"created": "Sun, 13 Oct 2024 15:08:49 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Nov 2024 11:11:00 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 08:02:31 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Park",
"Yein",
""
],
[
"Yoon",
"Chanwoong",
""
],
[
"Park",
"Jungwoo",
""
],
[
"Lee",
"Donghyeon",
""
],
[
"Jeong",
"Minbyul",
""
],
[
"Kang",
"Jaewoo",
""
]
] |
TITLE: ChroKnowledge: Unveiling Chronological Knowledge of Language Models in
Multiple Domains
ABSTRACT: Large language models (LLMs) have brought significant changes to many aspects
of our lives. However, assessing and ensuring their chronological knowledge
remains challenging. Existing approaches fall short in addressing the temporal
adaptability of knowledge, often relying on a fixed time-point view. To
overcome this, we introduce ChroKnowBench, a benchmark dataset designed to
evaluate chronologically accumulated knowledge across three key aspects:
multiple domains, time dependency, temporal state. Our benchmark distinguishes
between knowledge that evolves (e.g., personal history, scientific discoveries,
amended laws) and knowledge that remain constant (e.g., mathematical truths,
commonsense facts). Building on this benchmark, we present ChroKnowledge
(Chronological Categorization of Knowledge), a novel sampling-based framework
for evaluating LLMs' non-parametric chronological knowledge. Our evaluation led
to the following observations: (1) The ability of eliciting temporal knowledge
varies depending on the data format that model was trained on. (2) LLMs
partially recall knowledge or show a cut-off at temporal boundaries rather than
recalling all aspects of knowledge correctly. Thus, we apply our
ChroKnowPrompt, an in-depth prompting to elicit chronological knowledge by
traversing step-by-step through the surrounding time spans. We observe that it
successfully recalls objects across both open-source and proprietary LLMs,
demonstrating versatility, though it faces challenges with dynamic datasets and
unstructured formats.
|
new_dataset
| 0.958924
|
2410.10105
|
Qian Yu
|
Qian Yu, Peng-Tao Jiang, Hao Zhang, Jinwei Chen, Bo Li, Lihe Zhang,
Huchuan Lu
|
High-Precision Dichotomous Image Segmentation via Probing Diffusion
Capacity
|
Published as a conference paper at ICLR 2025
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the realm of high-resolution (HR), fine-grained image segmentation, the
primary challenge is balancing broad contextual awareness with the precision
required for detailed object delineation, capturing intricate details and the
finest edges of objects. Diffusion models, trained on vast datasets comprising
billions of image-text pairs, such as SD V2.1, have revolutionized
text-to-image synthesis by delivering exceptional quality, fine detail
resolution, and strong contextual awareness, making them an attractive solution
for high-resolution image segmentation. To this end, we propose DiffDIS, a
diffusion-driven segmentation model that taps into the potential of the
pre-trained U-Net within diffusion models, specifically designed for
high-resolution, fine-grained object segmentation. By leveraging the robust
generalization capabilities and rich, versatile image representation prior of
the SD models, coupled with a task-specific stable one-step denoising approach,
we significantly reduce the inference time while preserving high-fidelity,
detailed generation. Additionally, we introduce an auxiliary edge generation
task to not only enhance the preservation of fine details of the object
boundaries, but reconcile the probabilistic nature of diffusion with the
deterministic demands of segmentation. With these refined strategies in place,
DiffDIS serves as a rapid object mask generation model, specifically optimized
for generating detailed binary maps at high resolutions, while demonstrating
impressive accuracy and swift processing. Experiments on the DIS5K dataset
demonstrate the superiority of DiffDIS, achieving state-of-the-art results
through a streamlined inference process. The source code will be publicly
available at https://github.com/qianyu-dlut/DiffDIS.
|
[
{
"version": "v1",
"created": "Mon, 14 Oct 2024 02:49:23 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 09:44:00 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Yu",
"Qian",
""
],
[
"Jiang",
"Peng-Tao",
""
],
[
"Zhang",
"Hao",
""
],
[
"Chen",
"Jinwei",
""
],
[
"Li",
"Bo",
""
],
[
"Zhang",
"Lihe",
""
],
[
"Lu",
"Huchuan",
""
]
] |
TITLE: High-Precision Dichotomous Image Segmentation via Probing Diffusion
Capacity
ABSTRACT: In the realm of high-resolution (HR), fine-grained image segmentation, the
primary challenge is balancing broad contextual awareness with the precision
required for detailed object delineation, capturing intricate details and the
finest edges of objects. Diffusion models, trained on vast datasets comprising
billions of image-text pairs, such as SD V2.1, have revolutionized
text-to-image synthesis by delivering exceptional quality, fine detail
resolution, and strong contextual awareness, making them an attractive solution
for high-resolution image segmentation. To this end, we propose DiffDIS, a
diffusion-driven segmentation model that taps into the potential of the
pre-trained U-Net within diffusion models, specifically designed for
high-resolution, fine-grained object segmentation. By leveraging the robust
generalization capabilities and rich, versatile image representation prior of
the SD models, coupled with a task-specific stable one-step denoising approach,
we significantly reduce the inference time while preserving high-fidelity,
detailed generation. Additionally, we introduce an auxiliary edge generation
task to not only enhance the preservation of fine details of the object
boundaries, but reconcile the probabilistic nature of diffusion with the
deterministic demands of segmentation. With these refined strategies in place,
DiffDIS serves as a rapid object mask generation model, specifically optimized
for generating detailed binary maps at high resolutions, while demonstrating
impressive accuracy and swift processing. Experiments on the DIS5K dataset
demonstrate the superiority of DiffDIS, achieving state-of-the-art results
through a streamlined inference process. The source code will be publicly
available at https://github.com/qianyu-dlut/DiffDIS.
|
no_new_dataset
| 0.949389
|
2410.11540
|
Yaxin Du
|
Yaxin Du and Rui Ye and Fengting Yuchi and Wanru Zhao and Jingjing Qu
and Yanfeng Wang and Siheng Chen
|
Data Quality Control in Federated Instruction-tuning of Large Language
Models
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Federated Learning (FL) enables privacy-preserving collaborative instruction
tuning of large language models (LLMs) by leveraging massively distributed
data. However, the decentralized nature of FL exacerbates data quality
challenges, as local clients lack global visibility to filter noisy or
low-quality samples before training. To resolve this issue, we propose FedDQC,
a novel federated instruction tuning framework with dynamic data quality
control. Our approach introduces two key innovations. First, we propose
instruction-response alignment (IRA), an efficient client-side metric for
quality evaluation requiring only low-cost inference. We validate that
higher-IRA data corresponds to more relevant and easier-to-learn
question-answer pairs. Second, mirroring the human easy-to-hard knowledge
acquisition process, we design a quality-aware hierarchical FL training
framework, where the LLM is progressively fine-tuned from high- to low-IRA data
in a collaborative manner. The framework also supports adaptive data quality
assessment at each hierarchy, enabling dynamic adjustments throughout the
training process. Extensive experiments on synthetic and real-world datasets
show that our method significantly improves LLM performance on mixed-quality
data in FL.
|
[
{
"version": "v1",
"created": "Tue, 15 Oct 2024 12:14:57 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 14:35:58 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Du",
"Yaxin",
""
],
[
"Ye",
"Rui",
""
],
[
"Yuchi",
"Fengting",
""
],
[
"Zhao",
"Wanru",
""
],
[
"Qu",
"Jingjing",
""
],
[
"Wang",
"Yanfeng",
""
],
[
"Chen",
"Siheng",
""
]
] |
TITLE: Data Quality Control in Federated Instruction-tuning of Large Language
Models
ABSTRACT: Federated Learning (FL) enables privacy-preserving collaborative instruction
tuning of large language models (LLMs) by leveraging massively distributed
data. However, the decentralized nature of FL exacerbates data quality
challenges, as local clients lack global visibility to filter noisy or
low-quality samples before training. To resolve this issue, we propose FedDQC,
a novel federated instruction tuning framework with dynamic data quality
control. Our approach introduces two key innovations. First, we propose
instruction-response alignment (IRA), an efficient client-side metric for
quality evaluation requiring only low-cost inference. We validate that
higher-IRA data corresponds to more relevant and easier-to-learn
question-answer pairs. Second, mirroring the human easy-to-hard knowledge
acquisition process, we design a quality-aware hierarchical FL training
framework, where the LLM is progressively fine-tuned from high- to low-IRA data
in a collaborative manner. The framework also supports adaptive data quality
assessment at each hierarchy, enabling dynamic adjustments throughout the
training process. Extensive experiments on synthetic and real-world datasets
show that our method significantly improves LLM performance on mixed-quality
data in FL.
|
no_new_dataset
| 0.945901
|
2410.12207
|
Xianren Zhang
|
Xianren Zhang, Xianfeng Tang, Hui Liu, Zongyu Wu, Qi He, Dongwon Lee
and Suhang Wang
|
Divide-Verify-Refine: Can LLMs Self-Align with Complex Instructions?
|
Under review
| null | null | null |
cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Recent studies show LLMs struggle with complex instructions involving
multiple constraints (e.g., length, format, sentiment). Existing works address
this issue by fine-tuning, which heavily relies on fine-tuning data quality and
is computational expensive. An alternative is leveraging LLMs' self-correction
to refine responses for better constraint adherence. However, this is limited
by the feedback quality, as LLMs cannot generate reliable feedback or detect
errors. Moreover, its effectiveness relies on few-shot examples illustrating
response modifications. As constraints in complex instructions are diverse,
manually crafting such examples for each constraint type can be labor-intensive
and sub-optimal. To address these two challenges, we propose the
Divide-Verify-Refine (DVR) framework with three steps: (1) Divide complex
instructions into single constraints and prepare appropriate tools; (2) Verify
responses using tools that provide rigorous check and textual guidance (e.g.,
Python toolkit for format checks or pre-trained classifiers for content
analysis); (3) Refine: To maximize refinement effectiveness, we propose dynamic
few-shot prompting, where a refinement repository collects successful
refinements, and these examples are selectively retrieved for future
refinements. Recognizing the lack of complexity in existing datasets, we create
a new dataset of complex instructions. DVR doubles Llama3.1-8B's constraint
adherence and triples Mistral-7B's performance.
|
[
{
"version": "v1",
"created": "Wed, 16 Oct 2024 04:01:55 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Feb 2025 22:16:18 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Zhang",
"Xianren",
""
],
[
"Tang",
"Xianfeng",
""
],
[
"Liu",
"Hui",
""
],
[
"Wu",
"Zongyu",
""
],
[
"He",
"Qi",
""
],
[
"Lee",
"Dongwon",
""
],
[
"Wang",
"Suhang",
""
]
] |
TITLE: Divide-Verify-Refine: Can LLMs Self-Align with Complex Instructions?
ABSTRACT: Recent studies show LLMs struggle with complex instructions involving
multiple constraints (e.g., length, format, sentiment). Existing works address
this issue by fine-tuning, which heavily relies on fine-tuning data quality and
is computational expensive. An alternative is leveraging LLMs' self-correction
to refine responses for better constraint adherence. However, this is limited
by the feedback quality, as LLMs cannot generate reliable feedback or detect
errors. Moreover, its effectiveness relies on few-shot examples illustrating
response modifications. As constraints in complex instructions are diverse,
manually crafting such examples for each constraint type can be labor-intensive
and sub-optimal. To address these two challenges, we propose the
Divide-Verify-Refine (DVR) framework with three steps: (1) Divide complex
instructions into single constraints and prepare appropriate tools; (2) Verify
responses using tools that provide rigorous check and textual guidance (e.g.,
Python toolkit for format checks or pre-trained classifiers for content
analysis); (3) Refine: To maximize refinement effectiveness, we propose dynamic
few-shot prompting, where a refinement repository collects successful
refinements, and these examples are selectively retrieved for future
refinements. Recognizing the lack of complexity in existing datasets, we create
a new dataset of complex instructions. DVR doubles Llama3.1-8B's constraint
adherence and triples Mistral-7B's performance.
|
new_dataset
| 0.942082
|
2410.12337
|
Linfeng Xu
|
Linfeng Xu, Fanman Meng, Qingbo Wu, Lili Pan, Heqian Qiu, Lanxiao
Wang, Kailong Chen, Kanglei Geng, Yilei Qian, Haojie Wang, Shuchang Zhou,
Shimou Ling, Zejia Liu, Nanlin Chen, Yingjie Xu, Shaoxu Cheng, Bowen Tan,
Ziyong Xu, Hongliang Li
|
ARIC: An Activity Recognition Dataset in Classroom Surveillance Images
|
arXiv admin note: text overlap with arXiv:2409.03354. Updated the
description for ARIC supplement
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The application of activity recognition in the ``AI + Education" field is
gaining increasing attention. However, current work mainly focuses on the
recognition of activities in manually captured videos and a limited number of
activity types, with little attention given to recognizing activities in
surveillance images from real classrooms. Activity recognition in classroom
surveillance images faces multiple challenges, such as class imbalance and high
activity similarity. To address this gap, we constructed a novel multimodal
dataset focused on classroom surveillance image activity recognition called
ARIC (Activity Recognition In Classroom). The ARIC dataset has advantages of
multiple perspectives, 32 activity categories, three modalities, and real-world
classroom scenarios. In addition to the general activity recognition tasks, we
also provide settings for continual learning and few-shot continual learning.
We hope that the ARIC dataset can act as a facilitator for future analysis and
research for open teaching scenarios. You can download preliminary data from
https://ivipclab.github.io/publication_ARIC/ARIC.
|
[
{
"version": "v1",
"created": "Wed, 16 Oct 2024 07:59:07 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 12:45:25 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Xu",
"Linfeng",
""
],
[
"Meng",
"Fanman",
""
],
[
"Wu",
"Qingbo",
""
],
[
"Pan",
"Lili",
""
],
[
"Qiu",
"Heqian",
""
],
[
"Wang",
"Lanxiao",
""
],
[
"Chen",
"Kailong",
""
],
[
"Geng",
"Kanglei",
""
],
[
"Qian",
"Yilei",
""
],
[
"Wang",
"Haojie",
""
],
[
"Zhou",
"Shuchang",
""
],
[
"Ling",
"Shimou",
""
],
[
"Liu",
"Zejia",
""
],
[
"Chen",
"Nanlin",
""
],
[
"Xu",
"Yingjie",
""
],
[
"Cheng",
"Shaoxu",
""
],
[
"Tan",
"Bowen",
""
],
[
"Xu",
"Ziyong",
""
],
[
"Li",
"Hongliang",
""
]
] |
TITLE: ARIC: An Activity Recognition Dataset in Classroom Surveillance Images
ABSTRACT: The application of activity recognition in the ``AI + Education" field is
gaining increasing attention. However, current work mainly focuses on the
recognition of activities in manually captured videos and a limited number of
activity types, with little attention given to recognizing activities in
surveillance images from real classrooms. Activity recognition in classroom
surveillance images faces multiple challenges, such as class imbalance and high
activity similarity. To address this gap, we constructed a novel multimodal
dataset focused on classroom surveillance image activity recognition called
ARIC (Activity Recognition In Classroom). The ARIC dataset has advantages of
multiple perspectives, 32 activity categories, three modalities, and real-world
classroom scenarios. In addition to the general activity recognition tasks, we
also provide settings for continual learning and few-shot continual learning.
We hope that the ARIC dataset can act as a facilitator for future analysis and
research for open teaching scenarios. You can download preliminary data from
https://ivipclab.github.io/publication_ARIC/ARIC.
|
new_dataset
| 0.962356
|
2410.13322
|
Giovanni Braglia
|
Giovanni Braglia, Davide Tebaldi, Andr\'e Eugenio Lazzaretti and Luigi
Biagiotti
|
Arc-Length-Based Warping for Robot Skill Synthesis from Multiple
Demonstrations
|
8 pages, 7 figures
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
In robotics, Learning from Demonstration (LfD) aims to transfer skills to
robots by using multiple demonstrations of the same task. These demonstrations
are recorded and processed to extract a consistent skill representation. This
process typically requires temporal alignment through techniques such as
Dynamic Time Warping (DTW). In this paper, we consider a novel algorithm, named
Spatial Sampling (SS), specifically designed for robot trajectories, that
enables time-independent alignment of the trajectories by providing an
arc-length parametrization of the signals. This approach eliminates the need
for temporal alignment, enhancing the accuracy and robustness of skill
representation, especially when recorded movements are subject to intermittent
motions or extremely variable speeds, a common characteristic of operations
based on kinesthetic teaching, where the operator may encounter difficulties in
guiding the end-effector smoothly. To prove this, we built a custom publicly
available dataset of robot recordings to test real-world movements, where the
user tracks the same geometric path multiple times, with motion laws that vary
greatly and are subject to starting and stopping. The SS demonstrates better
performances against state-of-the-art algorithms in terms of (i) trajectory
synchronization and (ii) quality of the extracted skill.
|
[
{
"version": "v1",
"created": "Thu, 17 Oct 2024 08:25:44 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 15:25:53 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Braglia",
"Giovanni",
""
],
[
"Tebaldi",
"Davide",
""
],
[
"Lazzaretti",
"André Eugenio",
""
],
[
"Biagiotti",
"Luigi",
""
]
] |
TITLE: Arc-Length-Based Warping for Robot Skill Synthesis from Multiple
Demonstrations
ABSTRACT: In robotics, Learning from Demonstration (LfD) aims to transfer skills to
robots by using multiple demonstrations of the same task. These demonstrations
are recorded and processed to extract a consistent skill representation. This
process typically requires temporal alignment through techniques such as
Dynamic Time Warping (DTW). In this paper, we consider a novel algorithm, named
Spatial Sampling (SS), specifically designed for robot trajectories, that
enables time-independent alignment of the trajectories by providing an
arc-length parametrization of the signals. This approach eliminates the need
for temporal alignment, enhancing the accuracy and robustness of skill
representation, especially when recorded movements are subject to intermittent
motions or extremely variable speeds, a common characteristic of operations
based on kinesthetic teaching, where the operator may encounter difficulties in
guiding the end-effector smoothly. To prove this, we built a custom publicly
available dataset of robot recordings to test real-world movements, where the
user tracks the same geometric path multiple times, with motion laws that vary
greatly and are subject to starting and stopping. The SS demonstrates better
performances against state-of-the-art algorithms in terms of (i) trajectory
synchronization and (ii) quality of the extracted skill.
|
new_dataset
| 0.955194
|
2410.14668
|
Jie He
|
Xiongtao Zhou, Jie He, Lanyu Chen, Jingyu Li, Haojing Chen, V\'ictor
Guti\'errez-Basulto, Jeff Z. Pan, Hanjie Chen
|
MiCEval: Unveiling Multimodal Chain of Thought's Quality via Image
Description and Reasoning Steps
|
NAACL 2025
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Multimodal Chain of Thought (MCoT) is a popular prompting strategy for
improving the performance of multimodal large language models (MLLMs) across a
range of complex reasoning tasks. Despite its popularity, there is a notable
absence of automated methods for evaluating the quality of reasoning steps in
MCoT. To address this gap, we propose Multimodal Chain-of-Thought Evaluation
(MiCEval), a framework designed to assess the correctness of reasoning chains
by evaluating the quality of both the description and each reasoning step. The
evaluation of the description component focuses on the accuracy of the image
descriptions, while the reasoning step evaluates the quality of each step as it
is conditionally generated based on the preceding steps. MiCEval is built upon
a fine-grained dataset with annotations that rate each step according to
correctness, relevance, and informativeness. Extensive experiments on four
state-of-the-art MLLMs show that step-wise evaluations using MiCEval align more
closely with human judgments compared to existing methods based on cosine
similarity or fine-tuning approaches. MiCEval datasets and code can be found in
https://github.com/alenai97/MiCEval.
|
[
{
"version": "v1",
"created": "Fri, 18 Oct 2024 17:57:40 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Oct 2024 21:42:46 GMT"
},
{
"version": "v3",
"created": "Sat, 16 Nov 2024 18:47:18 GMT"
},
{
"version": "v4",
"created": "Fri, 28 Feb 2025 12:57:03 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Zhou",
"Xiongtao",
""
],
[
"He",
"Jie",
""
],
[
"Chen",
"Lanyu",
""
],
[
"Li",
"Jingyu",
""
],
[
"Chen",
"Haojing",
""
],
[
"Gutiérrez-Basulto",
"Víctor",
""
],
[
"Pan",
"Jeff Z.",
""
],
[
"Chen",
"Hanjie",
""
]
] |
TITLE: MiCEval: Unveiling Multimodal Chain of Thought's Quality via Image
Description and Reasoning Steps
ABSTRACT: Multimodal Chain of Thought (MCoT) is a popular prompting strategy for
improving the performance of multimodal large language models (MLLMs) across a
range of complex reasoning tasks. Despite its popularity, there is a notable
absence of automated methods for evaluating the quality of reasoning steps in
MCoT. To address this gap, we propose Multimodal Chain-of-Thought Evaluation
(MiCEval), a framework designed to assess the correctness of reasoning chains
by evaluating the quality of both the description and each reasoning step. The
evaluation of the description component focuses on the accuracy of the image
descriptions, while the reasoning step evaluates the quality of each step as it
is conditionally generated based on the preceding steps. MiCEval is built upon
a fine-grained dataset with annotations that rate each step according to
correctness, relevance, and informativeness. Extensive experiments on four
state-of-the-art MLLMs show that step-wise evaluations using MiCEval align more
closely with human judgments compared to existing methods based on cosine
similarity or fine-tuning approaches. MiCEval datasets and code can be found in
https://github.com/alenai97/MiCEval.
|
new_dataset
| 0.958654
|
2410.18148
|
Shaowu Pan
|
Nithin Somasekharan, Shaowu Pan
|
Beyond the Kolmogorov Barrier: A Learnable Weighted Hybrid Autoencoder
for Model Order Reduction
|
31 pages
| null | null | null |
cs.LG cs.AI physics.comp-ph stat.ML
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Representation learning for high-dimensional, complex physical systems aims
to identify a low-dimensional intrinsic latent space, which is crucial for
reduced-order modeling and modal analysis. To overcome the well-known
Kolmogorov barrier, deep autoencoders (AEs) have been introduced in recent
years, but they often suffer from poor convergence behavior as the rank of the
latent space increases. To address this issue, we propose the learnable
weighted hybrid autoencoder, a hybrid approach that combines the strengths of
singular value decomposition (SVD) with deep autoencoders through a learnable
weighted framework. We find that the introduction of learnable weighting
parameters is essential -- without them, the resulting model would either
collapse into a standard POD or fail to exhibit the desired convergence
behavior. Interestingly, we empirically find that our trained model has a
sharpness thousands of times smaller compared to other models. Our experiments
on classical chaotic PDE systems, including the 1D Kuramoto-Sivashinsky and
forced isotropic turbulence datasets, demonstrate that our approach
significantly improves generalization performance compared to several competing
methods. Additionally, when combining with time series modeling techniques
(e.g., Koopman operator, LSTM), the proposed technique offers significant
improvements for surrogate modeling of high-dimensional multi-scale PDE
systems.
|
[
{
"version": "v1",
"created": "Wed, 23 Oct 2024 00:04:26 GMT"
},
{
"version": "v2",
"created": "Sat, 22 Feb 2025 00:06:01 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 17:12:31 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Somasekharan",
"Nithin",
""
],
[
"Pan",
"Shaowu",
""
]
] |
TITLE: Beyond the Kolmogorov Barrier: A Learnable Weighted Hybrid Autoencoder
for Model Order Reduction
ABSTRACT: Representation learning for high-dimensional, complex physical systems aims
to identify a low-dimensional intrinsic latent space, which is crucial for
reduced-order modeling and modal analysis. To overcome the well-known
Kolmogorov barrier, deep autoencoders (AEs) have been introduced in recent
years, but they often suffer from poor convergence behavior as the rank of the
latent space increases. To address this issue, we propose the learnable
weighted hybrid autoencoder, a hybrid approach that combines the strengths of
singular value decomposition (SVD) with deep autoencoders through a learnable
weighted framework. We find that the introduction of learnable weighting
parameters is essential -- without them, the resulting model would either
collapse into a standard POD or fail to exhibit the desired convergence
behavior. Interestingly, we empirically find that our trained model has a
sharpness thousands of times smaller compared to other models. Our experiments
on classical chaotic PDE systems, including the 1D Kuramoto-Sivashinsky and
forced isotropic turbulence datasets, demonstrate that our approach
significantly improves generalization performance compared to several competing
methods. Additionally, when combining with time series modeling techniques
(e.g., Koopman operator, LSTM), the proposed technique offers significant
improvements for surrogate modeling of high-dimensional multi-scale PDE
systems.
|
no_new_dataset
| 0.949012
|
2410.18456
|
Bingyu Yang
|
Bingyu Yang, Qingyao Tian, Huai Liao, Xinyan Huang, Jinlin Wu, Jingdi
Hu, Hongbin Liu
|
Progressive Curriculum Learning with Scale-Enhanced U-Net for Continuous
Airway Segmentation
| null | null | null | null |
eess.IV cs.AI cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Continuous and accurate segmentation of airways in chest CT images is
essential for preoperative planning and real-time bronchoscopy navigation.
Despite advances in deep learning for medical image segmentation, maintaining
airway continuity remains a challenge, particularly due to intra-class
imbalance between large and small branches and blurred CT scan details. To
address these challenges, we propose a progressive curriculum learning pipeline
and a Scale-Enhanced U-Net (SE-UNet) to enhance segmentation continuity.
Specifically, our progressive curriculum learning pipeline consists of three
stages: extracting main airways, identifying small airways, and repairing
discontinuities. The cropping sampling strategy in each stage reduces feature
interference between airways of different scales, effectively addressing the
challenge of intra-class imbalance. In the third training stage, we present an
Adaptive Topology-Responsive Loss (ATRL) to guide the network to focus on
airway continuity. The progressive training pipeline shares the same SE-UNet,
integrating multi-scale inputs and Detail Information Enhancers (DIEs) to
enhance information flow and effectively capture the intricate details of small
airways. Additionally, we propose a robust airway tree parsing method and
hierarchical evaluation metrics to provide more clinically relevant and precise
analysis. Experiments on both in-house and public datasets demonstrate that our
method outperforms existing approaches, significantly improving the accuracy of
small airways and the completeness of the airway tree. The code will be
released upon publication.
|
[
{
"version": "v1",
"created": "Thu, 24 Oct 2024 06:10:09 GMT"
},
{
"version": "v2",
"created": "Sun, 10 Nov 2024 12:13:17 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 15:04:56 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Yang",
"Bingyu",
""
],
[
"Tian",
"Qingyao",
""
],
[
"Liao",
"Huai",
""
],
[
"Huang",
"Xinyan",
""
],
[
"Wu",
"Jinlin",
""
],
[
"Hu",
"Jingdi",
""
],
[
"Liu",
"Hongbin",
""
]
] |
TITLE: Progressive Curriculum Learning with Scale-Enhanced U-Net for Continuous
Airway Segmentation
ABSTRACT: Continuous and accurate segmentation of airways in chest CT images is
essential for preoperative planning and real-time bronchoscopy navigation.
Despite advances in deep learning for medical image segmentation, maintaining
airway continuity remains a challenge, particularly due to intra-class
imbalance between large and small branches and blurred CT scan details. To
address these challenges, we propose a progressive curriculum learning pipeline
and a Scale-Enhanced U-Net (SE-UNet) to enhance segmentation continuity.
Specifically, our progressive curriculum learning pipeline consists of three
stages: extracting main airways, identifying small airways, and repairing
discontinuities. The cropping sampling strategy in each stage reduces feature
interference between airways of different scales, effectively addressing the
challenge of intra-class imbalance. In the third training stage, we present an
Adaptive Topology-Responsive Loss (ATRL) to guide the network to focus on
airway continuity. The progressive training pipeline shares the same SE-UNet,
integrating multi-scale inputs and Detail Information Enhancers (DIEs) to
enhance information flow and effectively capture the intricate details of small
airways. Additionally, we propose a robust airway tree parsing method and
hierarchical evaluation metrics to provide more clinically relevant and precise
analysis. Experiments on both in-house and public datasets demonstrate that our
method outperforms existing approaches, significantly improving the accuracy of
small airways and the completeness of the airway tree. The code will be
released upon publication.
|
no_new_dataset
| 0.952131
|
2410.18514
|
Shen Nie
|
Shen Nie, Fengqi Zhu, Chao Du, Tianyu Pang, Qian Liu, Guangtao Zeng,
Min Lin, Chongxuan Li
|
Scaling up Masked Diffusion Models on Text
| null | null | null | null |
cs.AI cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Masked diffusion models (MDMs) have shown promise in language modeling, yet
their scalability and effectiveness in core language tasks, such as text
generation and language understanding, remain underexplored. This paper
establishes the first scaling law for MDMs, demonstrating a scaling rate
comparable to autoregressive models (ARMs) and a relatively small compute gap.
Motivated by their scalability, we train a family of MDMs with up to 1.1
billion (B) parameters to systematically evaluate their performance against
ARMs of comparable or larger sizes. Fully leveraging the probabilistic
formulation of MDMs, we propose a simple yet effective unsupervised
classifier-free guidance that effectively exploits large-scale unpaired data,
boosting performance for conditional inference. In language understanding, the
1.1B MDM outperforms the 1.1B TinyLlama model trained on the same data across
four of eight zero-shot benchmarks. Notably, it achieves competitive math
reasoning ability with the 7B Llama-2 model on the GSM8K dataset. In text
generation, MDMs with 16 times more pre-training time offer a flexible
trade-off against ARMs with the accelerated sampling technique KV-Cache: MDMs
match ARMs in performance while being 1.4 times faster during sampling.
Moreover, MDMs address challenging tasks for ARMs by effectively handling
bidirectional reasoning and adapting to temporal shifts in data. Notably, a
1.1B MDM breaks the reverse curse encountered by much larger ARMs with
significantly more data and computation, such as 13B Llama-2 and 175B GPT-3.
Our code is available at https://github.com/ML-GSAI/SMDM.
|
[
{
"version": "v1",
"created": "Thu, 24 Oct 2024 08:01:22 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Dec 2024 03:55:07 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 07:02:59 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Nie",
"Shen",
""
],
[
"Zhu",
"Fengqi",
""
],
[
"Du",
"Chao",
""
],
[
"Pang",
"Tianyu",
""
],
[
"Liu",
"Qian",
""
],
[
"Zeng",
"Guangtao",
""
],
[
"Lin",
"Min",
""
],
[
"Li",
"Chongxuan",
""
]
] |
TITLE: Scaling up Masked Diffusion Models on Text
ABSTRACT: Masked diffusion models (MDMs) have shown promise in language modeling, yet
their scalability and effectiveness in core language tasks, such as text
generation and language understanding, remain underexplored. This paper
establishes the first scaling law for MDMs, demonstrating a scaling rate
comparable to autoregressive models (ARMs) and a relatively small compute gap.
Motivated by their scalability, we train a family of MDMs with up to 1.1
billion (B) parameters to systematically evaluate their performance against
ARMs of comparable or larger sizes. Fully leveraging the probabilistic
formulation of MDMs, we propose a simple yet effective unsupervised
classifier-free guidance that effectively exploits large-scale unpaired data,
boosting performance for conditional inference. In language understanding, the
1.1B MDM outperforms the 1.1B TinyLlama model trained on the same data across
four of eight zero-shot benchmarks. Notably, it achieves competitive math
reasoning ability with the 7B Llama-2 model on the GSM8K dataset. In text
generation, MDMs with 16 times more pre-training time offer a flexible
trade-off against ARMs with the accelerated sampling technique KV-Cache: MDMs
match ARMs in performance while being 1.4 times faster during sampling.
Moreover, MDMs address challenging tasks for ARMs by effectively handling
bidirectional reasoning and adapting to temporal shifts in data. Notably, a
1.1B MDM breaks the reverse curse encountered by much larger ARMs with
significantly more data and computation, such as 13B Llama-2 and 175B GPT-3.
Our code is available at https://github.com/ML-GSAI/SMDM.
|
no_new_dataset
| 0.94256
|
2410.18868
|
Katharina Friedl
|
Katharina Friedl, No\'emie Jaquier, Jens Lundell, Tamim Asfour, Danica
Kragic
|
A Riemannian Framework for Learning Reduced-order Lagrangian Dynamics
|
28 pages, 16 figures. Accepted for publication in ICLR'25
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
By incorporating physical consistency as inductive bias, deep neural networks
display increased generalization capabilities and data efficiency in learning
nonlinear dynamic models. However, the complexity of these models generally
increases with the system dimensionality, requiring larger datasets, more
complex deep networks, and significant computational effort. We propose a novel
geometric network architecture to learn physically-consistent reduced-order
dynamic parameters that accurately describe the original high-dimensional
system behavior. This is achieved by building on recent advances in model-order
reduction and by adopting a Riemannian perspective to jointly learn a
non-linear structure-preserving latent space and the associated low-dimensional
dynamics. Our approach enables accurate long-term predictions of the
high-dimensional dynamics of rigid and deformable systems with increased data
efficiency by inferring interpretable and physically-plausible reduced
Lagrangian models.
|
[
{
"version": "v1",
"created": "Thu, 24 Oct 2024 15:53:21 GMT"
},
{
"version": "v2",
"created": "Fri, 29 Nov 2024 17:02:31 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 16:12:10 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Friedl",
"Katharina",
""
],
[
"Jaquier",
"Noémie",
""
],
[
"Lundell",
"Jens",
""
],
[
"Asfour",
"Tamim",
""
],
[
"Kragic",
"Danica",
""
]
] |
TITLE: A Riemannian Framework for Learning Reduced-order Lagrangian Dynamics
ABSTRACT: By incorporating physical consistency as inductive bias, deep neural networks
display increased generalization capabilities and data efficiency in learning
nonlinear dynamic models. However, the complexity of these models generally
increases with the system dimensionality, requiring larger datasets, more
complex deep networks, and significant computational effort. We propose a novel
geometric network architecture to learn physically-consistent reduced-order
dynamic parameters that accurately describe the original high-dimensional
system behavior. This is achieved by building on recent advances in model-order
reduction and by adopting a Riemannian perspective to jointly learn a
non-linear structure-preserving latent space and the associated low-dimensional
dynamics. Our approach enables accurate long-term predictions of the
high-dimensional dynamics of rigid and deformable systems with increased data
efficiency by inferring interpretable and physically-plausible reduced
Lagrangian models.
|
no_new_dataset
| 0.950411
|
2410.18967
|
Zhe Gan
|
Zhangheng Li, Keen You, Haotian Zhang, Di Feng, Harsh Agrawal, Xiujun
Li, Mohana Prasad Sathya Moorthy, Jeff Nichols, Yinfei Yang, Zhe Gan
|
Ferret-UI 2: Mastering Universal User Interface Understanding Across
Platforms
|
Accepted to ICLR 2025
| null | null | null |
cs.CV cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Building a generalist model for user interface (UI) understanding is
challenging due to various foundational issues, such as platform diversity,
resolution variation, and data limitation. In this paper, we introduce
Ferret-UI 2, a multimodal large language model (MLLM) designed for universal UI
understanding across a wide range of platforms, including iPhone, Android,
iPad, Webpage, and AppleTV. Building on the foundation of Ferret-UI, Ferret-UI
2 introduces three key innovations: support for multiple platform types,
high-resolution perception through adaptive scaling, and advanced task training
data generation powered by GPT-4o with set-of-mark visual prompting. These
advancements enable Ferret-UI 2 to perform complex, user-centered interactions,
making it highly versatile and adaptable for the expanding diversity of
platform ecosystems. Extensive empirical experiments on referring, grounding,
user-centric advanced tasks (comprising 9 subtasks $\times$ 5 platforms), GUIDE
next-action prediction dataset, and GUI-World multi-platform benchmark
demonstrate that Ferret-UI 2 significantly outperforms Ferret-UI, and also
shows strong cross-platform transfer capabilities.
|
[
{
"version": "v1",
"created": "Thu, 24 Oct 2024 17:58:31 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 00:29:14 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Li",
"Zhangheng",
""
],
[
"You",
"Keen",
""
],
[
"Zhang",
"Haotian",
""
],
[
"Feng",
"Di",
""
],
[
"Agrawal",
"Harsh",
""
],
[
"Li",
"Xiujun",
""
],
[
"Moorthy",
"Mohana Prasad Sathya",
""
],
[
"Nichols",
"Jeff",
""
],
[
"Yang",
"Yinfei",
""
],
[
"Gan",
"Zhe",
""
]
] |
TITLE: Ferret-UI 2: Mastering Universal User Interface Understanding Across
Platforms
ABSTRACT: Building a generalist model for user interface (UI) understanding is
challenging due to various foundational issues, such as platform diversity,
resolution variation, and data limitation. In this paper, we introduce
Ferret-UI 2, a multimodal large language model (MLLM) designed for universal UI
understanding across a wide range of platforms, including iPhone, Android,
iPad, Webpage, and AppleTV. Building on the foundation of Ferret-UI, Ferret-UI
2 introduces three key innovations: support for multiple platform types,
high-resolution perception through adaptive scaling, and advanced task training
data generation powered by GPT-4o with set-of-mark visual prompting. These
advancements enable Ferret-UI 2 to perform complex, user-centered interactions,
making it highly versatile and adaptable for the expanding diversity of
platform ecosystems. Extensive empirical experiments on referring, grounding,
user-centric advanced tasks (comprising 9 subtasks $\times$ 5 platforms), GUIDE
next-action prediction dataset, and GUI-World multi-platform benchmark
demonstrate that Ferret-UI 2 significantly outperforms Ferret-UI, and also
shows strong cross-platform transfer capabilities.
|
new_dataset
| 0.965544
|
2411.00418
|
Chenghua Huang
|
Chenghua Huang, Zhizhen Fan, Lu Wang, Fangkai Yang, Pu Zhao, Zeqi Lin,
Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
|
Self-Evolved Reward Learning for LLMs
|
23 pages,6 figures,Accepted to ICLR 2025
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reinforcement Learning from Human Feedback (RLHF) is a crucial technique for
aligning language models with human preferences, playing a pivotal role in the
success of conversational models like GPT-4, ChatGPT, and Llama 2. A core
challenge in employing RLHF lies in training a reliable reward model (RM),
which relies on high-quality labels typically provided by human experts or
advanced AI system. These methods can be costly and may introduce biases that
affect the language model's responses. As language models improve, human input
may become less effective in further enhancing their performance. In this
paper, we propose Self-Evolved Reward Learning (SER), a novel approach where
the RM generates additional training data to iteratively improve itself. We
conducted extensive experiments on multiple datasets such as HH-RLHF and
UltraFeedback, using models like Mistral and Llama 3, and compare SER against
various baselines. Our results demonstrate that even with limited
human-annotated data, learning from self-feedback can robustly enhance RM
performance, thereby boosting the capabilities of large language models (LLMs).
|
[
{
"version": "v1",
"created": "Fri, 1 Nov 2024 07:29:03 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 03:37:09 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Huang",
"Chenghua",
""
],
[
"Fan",
"Zhizhen",
""
],
[
"Wang",
"Lu",
""
],
[
"Yang",
"Fangkai",
""
],
[
"Zhao",
"Pu",
""
],
[
"Lin",
"Zeqi",
""
],
[
"Lin",
"Qingwei",
""
],
[
"Zhang",
"Dongmei",
""
],
[
"Rajmohan",
"Saravan",
""
],
[
"Zhang",
"Qi",
""
]
] |
TITLE: Self-Evolved Reward Learning for LLMs
ABSTRACT: Reinforcement Learning from Human Feedback (RLHF) is a crucial technique for
aligning language models with human preferences, playing a pivotal role in the
success of conversational models like GPT-4, ChatGPT, and Llama 2. A core
challenge in employing RLHF lies in training a reliable reward model (RM),
which relies on high-quality labels typically provided by human experts or
advanced AI system. These methods can be costly and may introduce biases that
affect the language model's responses. As language models improve, human input
may become less effective in further enhancing their performance. In this
paper, we propose Self-Evolved Reward Learning (SER), a novel approach where
the RM generates additional training data to iteratively improve itself. We
conducted extensive experiments on multiple datasets such as HH-RLHF and
UltraFeedback, using models like Mistral and Llama 3, and compare SER against
various baselines. Our results demonstrate that even with limited
human-annotated data, learning from self-feedback can robustly enhance RM
performance, thereby boosting the capabilities of large language models (LLMs).
|
no_new_dataset
| 0.944485
|
2411.03753
|
Zihan Yu
|
Zihan Yu, Jingtao Ding, Yong Li
|
Symbolic regression via MDLformer-guided search: from minimizing
prediction error to minimizing description length
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Symbolic regression, a task discovering the formula best fitting the given
data, is typically based on the heuristical search. These methods usually
update candidate formulas to obtain new ones with lower prediction errors
iteratively.However, since formulas with similar function shapes may have
completely different symbolic forms, the prediction error does not decrease
monotonously as the search approaches the target formula, causing the low
recovery rate of existing methods. To solve this problem, we propose a novel
search objective based on the minimum description length, which reflects the
distance from the target and decreases monotonically as the search approaches
the correct form of the target formula. To estimate the minimum description
length of any input data, we design a neural network, MDLformer, which enables
robust and scalable estimation through large-scale training. With the
MDLformer's output as the search objective, we implement a symbolic regression
method, SR4MDL, that can effectively recover the correct mathematical form of
the formula. Extensive experiments illustrate its excellent performance in
recovering formulas from data. Our method successfully recovers around 50
formulas across two benchmark datasets comprising 133 problems, outperforming
state-of-the-art methods by 43.92%. Experiments on 122 unseen black-box
problems further demonstrate its generalization performance. We release our
code at https://github.com/tsinghua-fib-lab/SR4MDL .
|
[
{
"version": "v1",
"created": "Wed, 6 Nov 2024 08:29:46 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 07:48:42 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Yu",
"Zihan",
""
],
[
"Ding",
"Jingtao",
""
],
[
"Li",
"Yong",
""
]
] |
TITLE: Symbolic regression via MDLformer-guided search: from minimizing
prediction error to minimizing description length
ABSTRACT: Symbolic regression, a task discovering the formula best fitting the given
data, is typically based on the heuristical search. These methods usually
update candidate formulas to obtain new ones with lower prediction errors
iteratively.However, since formulas with similar function shapes may have
completely different symbolic forms, the prediction error does not decrease
monotonously as the search approaches the target formula, causing the low
recovery rate of existing methods. To solve this problem, we propose a novel
search objective based on the minimum description length, which reflects the
distance from the target and decreases monotonically as the search approaches
the correct form of the target formula. To estimate the minimum description
length of any input data, we design a neural network, MDLformer, which enables
robust and scalable estimation through large-scale training. With the
MDLformer's output as the search objective, we implement a symbolic regression
method, SR4MDL, that can effectively recover the correct mathematical form of
the formula. Extensive experiments illustrate its excellent performance in
recovering formulas from data. Our method successfully recovers around 50
formulas across two benchmark datasets comprising 133 problems, outperforming
state-of-the-art methods by 43.92%. Experiments on 122 unseen black-box
problems further demonstrate its generalization performance. We release our
code at https://github.com/tsinghua-fib-lab/SR4MDL .
|
no_new_dataset
| 0.944587
|
2411.04847
|
Fujie Zhang
|
Fujie Zhang, Peiqi Yu, Biao Yi, Baolei Zhang, Tong Li, Zheli Liu
|
Prompt-Guided Internal States for Hallucination Detection of Large
Language Models
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Large Language Models (LLMs) have demonstrated remarkable capabilities across
a variety of tasks in different domains. However, they sometimes generate
responses that are logically coherent but factually incorrect or misleading,
which is known as LLM hallucinations. Data-driven supervised methods train
hallucination detectors by leveraging the internal states of LLMs, but
detectors trained on specific domains often struggle to generalize well to
other domains. In this paper, we aim to enhance the cross-domain performance of
supervised detectors with only in-domain data. We propose a novel framework,
prompt-guided internal states for hallucination detection of LLMs, namely
PRISM. By utilizing appropriate prompts to guide changes to the structure
related to text truthfulness in LLMs' internal states, we make this structure
more salient and consistent across texts from different domains. We integrated
our framework with existing hallucination detection methods and conducted
experiments on datasets from different domains. The experimental results
indicate that our framework significantly enhances the cross-domain
generalization of existing hallucination detection methods.
|
[
{
"version": "v1",
"created": "Thu, 7 Nov 2024 16:33:48 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 02:41:06 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Zhang",
"Fujie",
""
],
[
"Yu",
"Peiqi",
""
],
[
"Yi",
"Biao",
""
],
[
"Zhang",
"Baolei",
""
],
[
"Li",
"Tong",
""
],
[
"Liu",
"Zheli",
""
]
] |
TITLE: Prompt-Guided Internal States for Hallucination Detection of Large
Language Models
ABSTRACT: Large Language Models (LLMs) have demonstrated remarkable capabilities across
a variety of tasks in different domains. However, they sometimes generate
responses that are logically coherent but factually incorrect or misleading,
which is known as LLM hallucinations. Data-driven supervised methods train
hallucination detectors by leveraging the internal states of LLMs, but
detectors trained on specific domains often struggle to generalize well to
other domains. In this paper, we aim to enhance the cross-domain performance of
supervised detectors with only in-domain data. We propose a novel framework,
prompt-guided internal states for hallucination detection of LLMs, namely
PRISM. By utilizing appropriate prompts to guide changes to the structure
related to text truthfulness in LLMs' internal states, we make this structure
more salient and consistent across texts from different domains. We integrated
our framework with existing hallucination detection methods and conducted
experiments on datasets from different domains. The experimental results
indicate that our framework significantly enhances the cross-domain
generalization of existing hallucination detection methods.
|
no_new_dataset
| 0.953275
|
2411.05692
|
Abhisek Ray Mr.
|
Abhisek Ray and Ayush Raj and Maheshkumar H. Kolekar
|
Autoregressive Adaptive Hypergraph Transformer for Skeleton-based
Activity Recognition
|
Accepted to WACV 2025
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Extracting multiscale contextual information and higher-order correlations
among skeleton sequences using Graph Convolutional Networks (GCNs) alone is
inadequate for effective action classification. Hypergraph convolution
addresses the above issues but cannot harness the long-range dependencies. The
transformer proves to be effective in capturing these dependencies and making
complex contextual features accessible. We propose an Autoregressive Adaptive
HyperGraph Transformer (AutoregAd-HGformer) model for in-phase (autoregressive
and discrete) and out-phase (adaptive) hypergraph generation. The vector
quantized in-phase hypergraph equipped with powerful autoregressive learned
priors produces a more robust and informative representation suitable for
hyperedge formation. The out-phase hypergraph generator provides a
model-agnostic hyperedge learning technique to align the attributes with input
skeleton embedding. The hybrid (supervised and unsupervised) learning in
AutoregAd-HGformer explores the action-dependent feature along spatial,
temporal, and channel dimensions. The extensive experimental results and
ablation study indicate the superiority of our model over state-of-the-art
hypergraph architectures on the NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets.
|
[
{
"version": "v1",
"created": "Fri, 8 Nov 2024 16:45:52 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Feb 2025 19:34:59 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Ray",
"Abhisek",
""
],
[
"Raj",
"Ayush",
""
],
[
"Kolekar",
"Maheshkumar H.",
""
]
] |
TITLE: Autoregressive Adaptive Hypergraph Transformer for Skeleton-based
Activity Recognition
ABSTRACT: Extracting multiscale contextual information and higher-order correlations
among skeleton sequences using Graph Convolutional Networks (GCNs) alone is
inadequate for effective action classification. Hypergraph convolution
addresses the above issues but cannot harness the long-range dependencies. The
transformer proves to be effective in capturing these dependencies and making
complex contextual features accessible. We propose an Autoregressive Adaptive
HyperGraph Transformer (AutoregAd-HGformer) model for in-phase (autoregressive
and discrete) and out-phase (adaptive) hypergraph generation. The vector
quantized in-phase hypergraph equipped with powerful autoregressive learned
priors produces a more robust and informative representation suitable for
hyperedge formation. The out-phase hypergraph generator provides a
model-agnostic hyperedge learning technique to align the attributes with input
skeleton embedding. The hybrid (supervised and unsupervised) learning in
AutoregAd-HGformer explores the action-dependent feature along spatial,
temporal, and channel dimensions. The extensive experimental results and
ablation study indicate the superiority of our model over state-of-the-art
hypergraph architectures on the NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets.
|
no_new_dataset
| 0.949059
|
2411.06655
|
Shu Wang
|
Shu Wang, Lei Ji, Renxi Wang, Wenxiao Zhao, Haokun Liu, Yifan Hou,
Ying Nian Wu
|
Explore the Reasoning Capability of LLMs in the Chess Testbed
|
NAACL2025 Main Conference. Data and models are available:
https://mate-chess.github.io/
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reasoning is a central capability of human intelligence. In recent years,
with the advent of large-scale datasets, pretrained large language models have
emerged with new capabilities, including reasoning. However, these models still
struggle with long-term, complex reasoning tasks, such as playing chess. Based
on the observation that expert chess players employ a dual approach combining
long-term strategic play with short-term tactical play along with language
explanation, we propose improving the reasoning capability of large language
models in chess by integrating annotated strategy and tactic. Specifically, we
collect a dataset named MATE, which consists of 1 million chess positions with
candidate moves annotated by chess experts for strategy and tactics. We
finetune the LLaMA-3-8B model and compare it against state-of-the-art
commercial language models in the task of selecting better chess moves. Our
experiments show that our models perform better than GPT, Claude, and Gemini
models. We find that language explanations can enhance the reasoning capability
of large language models.
|
[
{
"version": "v1",
"created": "Mon, 11 Nov 2024 01:42:56 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 11:58:28 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Wang",
"Shu",
""
],
[
"Ji",
"Lei",
""
],
[
"Wang",
"Renxi",
""
],
[
"Zhao",
"Wenxiao",
""
],
[
"Liu",
"Haokun",
""
],
[
"Hou",
"Yifan",
""
],
[
"Wu",
"Ying Nian",
""
]
] |
TITLE: Explore the Reasoning Capability of LLMs in the Chess Testbed
ABSTRACT: Reasoning is a central capability of human intelligence. In recent years,
with the advent of large-scale datasets, pretrained large language models have
emerged with new capabilities, including reasoning. However, these models still
struggle with long-term, complex reasoning tasks, such as playing chess. Based
on the observation that expert chess players employ a dual approach combining
long-term strategic play with short-term tactical play along with language
explanation, we propose improving the reasoning capability of large language
models in chess by integrating annotated strategy and tactic. Specifically, we
collect a dataset named MATE, which consists of 1 million chess positions with
candidate moves annotated by chess experts for strategy and tactics. We
finetune the LLaMA-3-8B model and compare it against state-of-the-art
commercial language models in the task of selecting better chess moves. Our
experiments show that our models perform better than GPT, Claude, and Gemini
models. We find that language explanations can enhance the reasoning capability
of large language models.
|
new_dataset
| 0.960621
|
2411.11285
|
Ranjan Sapkota
|
Ranjan Sapkota, Achyut Paudel, Manoj Karkee
|
Zero-Shot Automatic Annotation and Instance Segmentation using
LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for
Deep Learning Model Development
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Currently, deep learning-based instance segmentation for various applications
(e.g., Agriculture) is predominantly performed using a labor-intensive process
involving extensive field data collection using sophisticated sensors, followed
by careful manual annotation of images, presenting significant logistical and
financial challenges to researchers and organizations. The process also slows
down the model development and training process. In this study, we presented a
novel method for deep learning-based instance segmentation of apples in
commercial orchards that eliminates the need for labor-intensive field data
collection and manual annotation. Utilizing a Large Language Model (LLM), we
synthetically generated orchard images and automatically annotated them using
the Segment Anything Model (SAM) integrated with a YOLO11 base model. This
method significantly reduces reliance on physical sensors and manual data
processing, presenting a major advancement in "Agricultural AI". The synthetic,
auto-annotated dataset was used to train the YOLO11 model for Apple instance
segmentation, which was then validated on real orchard images. The results
showed that the automatically generated annotations achieved a Dice Coefficient
of 0.9513 and an IoU of 0.9303, validating the accuracy and overlap of the mask
annotations. All YOLO11 configurations, trained solely on these synthetic
datasets with automated annotations, accurately recognized and delineated
apples, highlighting the method's efficacy. Specifically, the YOLO11m-seg
configuration achieved a mask precision of 0.902 and a mask mAP@50 of 0.833 on
test images collected from a commercial orchard. Additionally, the YOLO11l-seg
configuration outperformed other models in validation on 40 LLM-generated
images, achieving the highest mask precision and mAP@50 metrics.
Keywords: YOLO, SAM, SAMv2, YOLO11, YOLOv11, Segment Anything, YOLO-SAM
|
[
{
"version": "v1",
"created": "Mon, 18 Nov 2024 05:11:29 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 00:44:36 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Sapkota",
"Ranjan",
""
],
[
"Paudel",
"Achyut",
""
],
[
"Karkee",
"Manoj",
""
]
] |
TITLE: Zero-Shot Automatic Annotation and Instance Segmentation using
LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for
Deep Learning Model Development
ABSTRACT: Currently, deep learning-based instance segmentation for various applications
(e.g., Agriculture) is predominantly performed using a labor-intensive process
involving extensive field data collection using sophisticated sensors, followed
by careful manual annotation of images, presenting significant logistical and
financial challenges to researchers and organizations. The process also slows
down the model development and training process. In this study, we presented a
novel method for deep learning-based instance segmentation of apples in
commercial orchards that eliminates the need for labor-intensive field data
collection and manual annotation. Utilizing a Large Language Model (LLM), we
synthetically generated orchard images and automatically annotated them using
the Segment Anything Model (SAM) integrated with a YOLO11 base model. This
method significantly reduces reliance on physical sensors and manual data
processing, presenting a major advancement in "Agricultural AI". The synthetic,
auto-annotated dataset was used to train the YOLO11 model for Apple instance
segmentation, which was then validated on real orchard images. The results
showed that the automatically generated annotations achieved a Dice Coefficient
of 0.9513 and an IoU of 0.9303, validating the accuracy and overlap of the mask
annotations. All YOLO11 configurations, trained solely on these synthetic
datasets with automated annotations, accurately recognized and delineated
apples, highlighting the method's efficacy. Specifically, the YOLO11m-seg
configuration achieved a mask precision of 0.902 and a mask mAP@50 of 0.833 on
test images collected from a commercial orchard. Additionally, the YOLO11l-seg
configuration outperformed other models in validation on 40 LLM-generated
images, achieving the highest mask precision and mAP@50 metrics.
Keywords: YOLO, SAM, SAMv2, YOLO11, YOLOv11, Segment Anything, YOLO-SAM
|
no_new_dataset
| 0.945601
|
2411.17645
|
Yujie Dai
|
Yujie Dai, Brian Sullivan, Axel Montout, Amy Dillon, Chris Waller,
Peter Acs, Rachel Denholm, Philip Williams, Alastair D Hay, Raul
Santos-Rodriguez, Andrew Dowsey
|
Explainable AI for Classifying UTI Risk Groups Using a Real-World Linked
EHR and Pathology Lab Dataset
| null | null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The use of machine learning and AI on electronic health records (EHRs) holds
substantial potential for clinical insight. However, this approach faces
challenges due to data heterogeneity, sparsity, temporal misalignment, and
limited labeled outcomes. In this context, we leverage a linked EHR dataset of
approximately one million de-identified individuals from Bristol, North
Somerset, and South Gloucestershire, UK, to characterize urinary tract
infections (UTIs). We implemented a data pre-processing and curation pipeline
that transforms the raw EHR data into a structured format suitable for
developing predictive models focused on data fairness, accountability and
transparency. Given the limited availability and biases of ground truth UTI
outcomes, we introduce a UTI risk estimation framework informed by clinical
expertise to estimate UTI risk across individual patient timelines. Pairwise
XGBoost models are trained using this framework to differentiate UTI risk
categories with explainable AI techniques applied to identify key predictors
and support interpretability. Our findings reveal differences in clinical and
demographic predictors across risk groups. While this study highlights the
potential of AI-driven insights to support UTI clinical decision-making,
further investigation of patient sub-strata and extensive validation are needed
to ensure robustness and applicability in clinical practice.
|
[
{
"version": "v1",
"created": "Tue, 26 Nov 2024 18:10:51 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Jan 2025 16:01:14 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 15:16:36 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Dai",
"Yujie",
""
],
[
"Sullivan",
"Brian",
""
],
[
"Montout",
"Axel",
""
],
[
"Dillon",
"Amy",
""
],
[
"Waller",
"Chris",
""
],
[
"Acs",
"Peter",
""
],
[
"Denholm",
"Rachel",
""
],
[
"Williams",
"Philip",
""
],
[
"Hay",
"Alastair D",
""
],
[
"Santos-Rodriguez",
"Raul",
""
],
[
"Dowsey",
"Andrew",
""
]
] |
TITLE: Explainable AI for Classifying UTI Risk Groups Using a Real-World Linked
EHR and Pathology Lab Dataset
ABSTRACT: The use of machine learning and AI on electronic health records (EHRs) holds
substantial potential for clinical insight. However, this approach faces
challenges due to data heterogeneity, sparsity, temporal misalignment, and
limited labeled outcomes. In this context, we leverage a linked EHR dataset of
approximately one million de-identified individuals from Bristol, North
Somerset, and South Gloucestershire, UK, to characterize urinary tract
infections (UTIs). We implemented a data pre-processing and curation pipeline
that transforms the raw EHR data into a structured format suitable for
developing predictive models focused on data fairness, accountability and
transparency. Given the limited availability and biases of ground truth UTI
outcomes, we introduce a UTI risk estimation framework informed by clinical
expertise to estimate UTI risk across individual patient timelines. Pairwise
XGBoost models are trained using this framework to differentiate UTI risk
categories with explainable AI techniques applied to identify key predictors
and support interpretability. Our findings reveal differences in clinical and
demographic predictors across risk groups. While this study highlights the
potential of AI-driven insights to support UTI clinical decision-making,
further investigation of patient sub-strata and extensive validation are needed
to ensure robustness and applicability in clinical practice.
|
no_new_dataset
| 0.945349
|
2412.02370
|
Eerik Alamikkotervo
|
Eerik Alamikkotervo, Henrik Toikka, Kari Tammi, Risto Ojala
|
Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter
Conditions
|
Small bugs fixed, noise filtering removed as it was removing useful
points, failure case analysis added, dataset published
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Robust road segmentation in all road conditions is required for safe
autonomous driving and advanced driver assistance systems. Supervised deep
learning methods provide accurate road segmentation in the domain of their
training data but cannot be trusted in out-of-distribution scenarios. Including
the whole distribution in the trainset is challenging as each sample must be
labeled by hand. Trajectory-based self-supervised methods offer a potential
solution as they can learn from the traversed route without manual labels.
However, existing trajectory-based methods use learning schemes that rely only
on the camera or only on the lidar. In this paper, trajectory-based learning is
implemented jointly with lidar and camera for increased performance. Our method
outperforms recent standalone camera- and lidar-based methods when evaluated
with a challenging winter driving dataset including countryside and suburb
driving scenes. The source code is available at
https://github.com/eerik98/lidar-camera-road-autolabeling.git
|
[
{
"version": "v1",
"created": "Tue, 3 Dec 2024 10:54:37 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 12:28:56 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Alamikkotervo",
"Eerik",
""
],
[
"Toikka",
"Henrik",
""
],
[
"Tammi",
"Kari",
""
],
[
"Ojala",
"Risto",
""
]
] |
TITLE: Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter
Conditions
ABSTRACT: Robust road segmentation in all road conditions is required for safe
autonomous driving and advanced driver assistance systems. Supervised deep
learning methods provide accurate road segmentation in the domain of their
training data but cannot be trusted in out-of-distribution scenarios. Including
the whole distribution in the trainset is challenging as each sample must be
labeled by hand. Trajectory-based self-supervised methods offer a potential
solution as they can learn from the traversed route without manual labels.
However, existing trajectory-based methods use learning schemes that rely only
on the camera or only on the lidar. In this paper, trajectory-based learning is
implemented jointly with lidar and camera for increased performance. Our method
outperforms recent standalone camera- and lidar-based methods when evaluated
with a challenging winter driving dataset including countryside and suburb
driving scenes. The source code is available at
https://github.com/eerik98/lidar-camera-road-autolabeling.git
|
no_new_dataset
| 0.945901
|
2412.03084
|
Deep Gupta Dr.
|
Ajinkya Deshpande, Deep Gupta, Ankit Bhurane, Nisha Meshram, Sneha
Singh, Petia Radeva
|
Hybrid deep learning-based strategy for the hepatocellular carcinoma
cancer grade classification of H&E stained liver histopathology images
|
14 figure, 9 tables
| null | null | null |
eess.IV cs.CV cs.LG q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
Hepatocellular carcinoma (HCC) is a common type of liver cancer whose
early-stage diagnosis is a common challenge, mainly due to the manual
assessment of hematoxylin and eosin-stained whole slide images, which is a
time-consuming process and may lead to variability in decision-making. For
accurate detection of HCC, we propose a hybrid deep learning-based architecture
that uses transfer learning to extract the features from pre-trained
convolutional neural network (CNN) models and a classifier made up of a
sequence of fully connected layers. This study uses a publicly available The
Cancer Genome Atlas Hepatocellular Carcinoma (TCGA-LIHC)database (n=491) for
model development and database of Kasturba Gandhi Medical College (KMC), India
for validation. The pre-processing step involves patch extraction, colour
normalization, and augmentation that results in 3920 patches for the TCGA
dataset. The developed hybrid deep neural network consisting of a CNN-based
pre-trained feature extractor and a customized artificial neural network-based
classifier is trained using five-fold cross-validation. For this study, eight
different state-of-the-art models are trained and tested as feature extractors
for the proposed hybrid model. The proposed hybrid model with ResNet50-based
feature extractor provided the sensitivity, specificity, F1-score, accuracy,
and AUC of 100.00%, 100.00%, 100.00%, 100.00%, and 1.00, respectively on the
TCGA database. On the KMC database, EfficientNetb3 resulted in the optimal
choice of the feature extractor giving sensitivity, specificity, F1-score,
accuracy, and AUC of 96.97, 98.85, 96.71, 96.71, and 0.99, respectively. The
proposed hybrid models showed improvement in accuracy of 2% and 4% over the
pre-trained models in TCGA-LIHC and KMC databases.
|
[
{
"version": "v1",
"created": "Wed, 4 Dec 2024 07:26:36 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 12:24:33 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Deshpande",
"Ajinkya",
""
],
[
"Gupta",
"Deep",
""
],
[
"Bhurane",
"Ankit",
""
],
[
"Meshram",
"Nisha",
""
],
[
"Singh",
"Sneha",
""
],
[
"Radeva",
"Petia",
""
]
] |
TITLE: Hybrid deep learning-based strategy for the hepatocellular carcinoma
cancer grade classification of H&E stained liver histopathology images
ABSTRACT: Hepatocellular carcinoma (HCC) is a common type of liver cancer whose
early-stage diagnosis is a common challenge, mainly due to the manual
assessment of hematoxylin and eosin-stained whole slide images, which is a
time-consuming process and may lead to variability in decision-making. For
accurate detection of HCC, we propose a hybrid deep learning-based architecture
that uses transfer learning to extract the features from pre-trained
convolutional neural network (CNN) models and a classifier made up of a
sequence of fully connected layers. This study uses a publicly available The
Cancer Genome Atlas Hepatocellular Carcinoma (TCGA-LIHC)database (n=491) for
model development and database of Kasturba Gandhi Medical College (KMC), India
for validation. The pre-processing step involves patch extraction, colour
normalization, and augmentation that results in 3920 patches for the TCGA
dataset. The developed hybrid deep neural network consisting of a CNN-based
pre-trained feature extractor and a customized artificial neural network-based
classifier is trained using five-fold cross-validation. For this study, eight
different state-of-the-art models are trained and tested as feature extractors
for the proposed hybrid model. The proposed hybrid model with ResNet50-based
feature extractor provided the sensitivity, specificity, F1-score, accuracy,
and AUC of 100.00%, 100.00%, 100.00%, 100.00%, and 1.00, respectively on the
TCGA database. On the KMC database, EfficientNetb3 resulted in the optimal
choice of the feature extractor giving sensitivity, specificity, F1-score,
accuracy, and AUC of 96.97, 98.85, 96.71, 96.71, and 0.99, respectively. The
proposed hybrid models showed improvement in accuracy of 2% and 4% over the
pre-trained models in TCGA-LIHC and KMC databases.
|
no_new_dataset
| 0.957636
|
2412.03844
|
Jiaqi Gu
|
Jingyu Lin, Jiaqi Gu, Lubin Fan, Bojian Wu, Yujing Lou, Renjie Chen,
Ligang Liu, Jieping Ye
|
HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian
Splatting
|
Accpeted by CVPR 2025. Project page:
https://gujiaqivadin.github.io/hybridgs/ Code:
https://github.com/Yeyuqqwx/HybridGS Data:
https://huggingface.co/Eto63277/HybridGS/tree/main
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Generating high-quality novel view renderings of 3D Gaussian Splatting (3DGS)
in scenes featuring transient objects is challenging. We propose a novel hybrid
representation, termed as HybridGS, using 2D Gaussians for transient objects
per image and maintaining traditional 3D Gaussians for the whole static scenes.
Note that, the 3DGS itself is better suited for modeling static scenes that
assume multi-view consistency, but the transient objects appear occasionally
and do not adhere to the assumption, thus we model them as planar objects from
a single view, represented with 2D Gaussians. Our novel representation
decomposes the scene from the perspective of fundamental viewpoint consistency,
making it more reasonable. Additionally, we present a novel multi-view
regulated supervision method for 3DGS that leverages information from
co-visible regions, further enhancing the distinctions between the transients
and statics. Then, we propose a straightforward yet effective multi-stage
training strategy to ensure robust training and high-quality view synthesis
across various settings. Experiments on benchmark datasets show our
state-of-the-art performance of novel view synthesis in both indoor and outdoor
scenes, even in the presence of distracting elements.
|
[
{
"version": "v1",
"created": "Thu, 5 Dec 2024 03:20:35 GMT"
},
{
"version": "v2",
"created": "Tue, 10 Dec 2024 04:59:24 GMT"
},
{
"version": "v3",
"created": "Thu, 27 Feb 2025 02:48:54 GMT"
},
{
"version": "v4",
"created": "Fri, 28 Feb 2025 09:49:45 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Lin",
"Jingyu",
""
],
[
"Gu",
"Jiaqi",
""
],
[
"Fan",
"Lubin",
""
],
[
"Wu",
"Bojian",
""
],
[
"Lou",
"Yujing",
""
],
[
"Chen",
"Renjie",
""
],
[
"Liu",
"Ligang",
""
],
[
"Ye",
"Jieping",
""
]
] |
TITLE: HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian
Splatting
ABSTRACT: Generating high-quality novel view renderings of 3D Gaussian Splatting (3DGS)
in scenes featuring transient objects is challenging. We propose a novel hybrid
representation, termed as HybridGS, using 2D Gaussians for transient objects
per image and maintaining traditional 3D Gaussians for the whole static scenes.
Note that, the 3DGS itself is better suited for modeling static scenes that
assume multi-view consistency, but the transient objects appear occasionally
and do not adhere to the assumption, thus we model them as planar objects from
a single view, represented with 2D Gaussians. Our novel representation
decomposes the scene from the perspective of fundamental viewpoint consistency,
making it more reasonable. Additionally, we present a novel multi-view
regulated supervision method for 3DGS that leverages information from
co-visible regions, further enhancing the distinctions between the transients
and statics. Then, we propose a straightforward yet effective multi-stage
training strategy to ensure robust training and high-quality view synthesis
across various settings. Experiments on benchmark datasets show our
state-of-the-art performance of novel view synthesis in both indoor and outdoor
scenes, even in the presence of distracting elements.
|
no_new_dataset
| 0.944331
|
2412.06071
|
Juyong Jiang
|
Fan Wang, Juyong Jiang, Chansung Park, Sunghun Kim, Jing Tang
|
KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models
|
The first three authors contributed equally to this work; Accepted by
ICLR 2025
| null | null | null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The increasing sizes of large language models (LLMs) result in significant
computational overhead and memory usage when adapting these models to specific
tasks or domains. Various parameter-efficient fine-tuning (PEFT) methods have
been devised to mitigate these challenges by training a small set of parameters
for the task-specific updates of the model weights. Among PEFT methods, LoRA
stands out for its simplicity and efficiency, inspiring the development of a
series of variants. However, LoRA and its successors disregard the knowledge
that is noisy or irrelevant to the targeted task, detrimentally impacting model
performance and leading to suboptimality. To address this limitation, we
introduce Knowledge-aware Singular-value Adaptation (KaSA), a PEFT method that
leverages singular value decomposition (SVD) with knowledge-aware singular
values to dynamically activate knowledge based on its relevance to the task at
hand. We conduct extensive experiments across a range of LLMs on tasks spanning
natural language understanding (NLU), generation (NLG), instruction following,
and commonsense reasoning. The experimental results demonstrate that KaSA
consistently outperforms FFT and 14 popular PEFT baselines across 16 benchmarks
and 4 synthetic datasets, underscoring our method's efficacy and adaptability.
The source code of our method is available at
https://github.com/juyongjiang/KaSA.
|
[
{
"version": "v1",
"created": "Sun, 8 Dec 2024 21:26:22 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 05:46:45 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Wang",
"Fan",
""
],
[
"Jiang",
"Juyong",
""
],
[
"Park",
"Chansung",
""
],
[
"Kim",
"Sunghun",
""
],
[
"Tang",
"Jing",
""
]
] |
TITLE: KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models
ABSTRACT: The increasing sizes of large language models (LLMs) result in significant
computational overhead and memory usage when adapting these models to specific
tasks or domains. Various parameter-efficient fine-tuning (PEFT) methods have
been devised to mitigate these challenges by training a small set of parameters
for the task-specific updates of the model weights. Among PEFT methods, LoRA
stands out for its simplicity and efficiency, inspiring the development of a
series of variants. However, LoRA and its successors disregard the knowledge
that is noisy or irrelevant to the targeted task, detrimentally impacting model
performance and leading to suboptimality. To address this limitation, we
introduce Knowledge-aware Singular-value Adaptation (KaSA), a PEFT method that
leverages singular value decomposition (SVD) with knowledge-aware singular
values to dynamically activate knowledge based on its relevance to the task at
hand. We conduct extensive experiments across a range of LLMs on tasks spanning
natural language understanding (NLU), generation (NLG), instruction following,
and commonsense reasoning. The experimental results demonstrate that KaSA
consistently outperforms FFT and 14 popular PEFT baselines across 16 benchmarks
and 4 synthetic datasets, underscoring our method's efficacy and adaptability.
The source code of our method is available at
https://github.com/juyongjiang/KaSA.
|
no_new_dataset
| 0.943348
|
2412.08467
|
Zun Wang
|
Zun Wang, Jialu Li, Yicong Hong, Songze Li, Kunchang Li, Shoubin Yu,
Yi Wang, Yu Qiao, Yali Wang, Mohit Bansal, Limin Wang
|
Bootstrapping Language-Guided Navigation Learning with Self-Refining
Data Flywheel
|
28 pages, Code and data are available at
https://github.com/wz0919/VLN-SRDF
| null | null | null |
cs.CV cs.AI cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Creating high-quality data for training robust language-instructed agents is
a long-lasting challenge in embodied AI. In this paper, we introduce a
Self-Refining Data Flywheel (SRDF) that generates high-quality and large-scale
navigational instruction-trajectory pairs by iteratively refining the data pool
through the collaboration between two models, the instruction generator and the
navigator, without any human-in-the-loop annotation. Specifically, SRDF starts
with using a base generator to create an initial data pool for training a base
navigator, followed by applying the trained navigator to filter the data pool.
This leads to higher-fidelity data to train a better generator, which can, in
turn, produce higher-quality data for training the next-round navigator. Such a
flywheel establishes a data self-refining process, yielding a continuously
improved and highly effective dataset for large-scale language-guided
navigation learning. Our experiments demonstrate that after several flywheel
rounds, the navigator elevates the performance boundary from 70% to 78% SPL on
the classic R2R test set, surpassing human performance (76%) for the first
time. Meanwhile, this process results in a superior generator, evidenced by a
SPICE increase from 23.5 to 26.2, better than all previous VLN instruction
generation methods. Finally, we demonstrate the scalability of our method
through increasing environment and instruction diversity, and the
generalization ability of our pre-trained navigator across various downstream
navigation tasks, surpassing state-of-the-art methods by a large margin in all
cases.
|
[
{
"version": "v1",
"created": "Wed, 11 Dec 2024 15:32:24 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 08:06:39 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Wang",
"Zun",
""
],
[
"Li",
"Jialu",
""
],
[
"Hong",
"Yicong",
""
],
[
"Li",
"Songze",
""
],
[
"Li",
"Kunchang",
""
],
[
"Yu",
"Shoubin",
""
],
[
"Wang",
"Yi",
""
],
[
"Qiao",
"Yu",
""
],
[
"Wang",
"Yali",
""
],
[
"Bansal",
"Mohit",
""
],
[
"Wang",
"Limin",
""
]
] |
TITLE: Bootstrapping Language-Guided Navigation Learning with Self-Refining
Data Flywheel
ABSTRACT: Creating high-quality data for training robust language-instructed agents is
a long-lasting challenge in embodied AI. In this paper, we introduce a
Self-Refining Data Flywheel (SRDF) that generates high-quality and large-scale
navigational instruction-trajectory pairs by iteratively refining the data pool
through the collaboration between two models, the instruction generator and the
navigator, without any human-in-the-loop annotation. Specifically, SRDF starts
with using a base generator to create an initial data pool for training a base
navigator, followed by applying the trained navigator to filter the data pool.
This leads to higher-fidelity data to train a better generator, which can, in
turn, produce higher-quality data for training the next-round navigator. Such a
flywheel establishes a data self-refining process, yielding a continuously
improved and highly effective dataset for large-scale language-guided
navigation learning. Our experiments demonstrate that after several flywheel
rounds, the navigator elevates the performance boundary from 70% to 78% SPL on
the classic R2R test set, surpassing human performance (76%) for the first
time. Meanwhile, this process results in a superior generator, evidenced by a
SPICE increase from 23.5 to 26.2, better than all previous VLN instruction
generation methods. Finally, we demonstrate the scalability of our method
through increasing environment and instruction diversity, and the
generalization ability of our pre-trained navigator across various downstream
navigation tasks, surpassing state-of-the-art methods by a large margin in all
cases.
|
no_new_dataset
| 0.945349
|
2412.11441
|
Yuning Han
|
Yuning Han, Bingyin Zhao, Rui Chu, Feng Luo, Biplab Sikdar, Yingjie
Lao
|
UIBDiffusion: Universal Imperceptible Backdoor Attack for Diffusion
Models
| null | null | null | null |
cs.CR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Recent studies show that diffusion models (DMs) are vulnerable to backdoor
attacks. Existing backdoor attacks impose unconcealed triggers (e.g., a gray
box and eyeglasses) that contain evident patterns, rendering remarkable attack
effects yet easy detection upon human inspection and defensive algorithms.
While it is possible to improve stealthiness by reducing the strength of the
backdoor, doing so can significantly compromise its generality and
effectiveness. In this paper, we propose UIBDiffusion, the universal
imperceptible backdoor attack for diffusion models, which allows us to achieve
superior attack and generation performance while evading state-of-the-art
defenses. We propose a novel trigger generation approach based on universal
adversarial perturbations (UAPs) and reveal that such perturbations, which are
initially devised for fooling pre-trained discriminative models, can be adapted
as potent imperceptible backdoor triggers for DMs. We evaluate UIBDiffusion on
multiple types of DMs with different kinds of samplers across various datasets
and targets. Experimental results demonstrate that UIBDiffusion brings three
advantages: 1) Universality, the imperceptible trigger is universal (i.e.,
image and model agnostic) where a single trigger is effective to any images and
all diffusion models with different samplers; 2) Utility, it achieves
comparable generation quality (e.g., FID) and even better attack success rate
(i.e., ASR) at low poison rates compared to the prior works; and 3)
Undetectability, UIBDiffusion is plausible to human perception and can bypass
Elijah and TERD, the SOTA defenses against backdoors for DMs. We will release
our backdoor triggers and code.
|
[
{
"version": "v1",
"created": "Mon, 16 Dec 2024 04:47:55 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Dec 2024 05:07:06 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 04:36:39 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Han",
"Yuning",
""
],
[
"Zhao",
"Bingyin",
""
],
[
"Chu",
"Rui",
""
],
[
"Luo",
"Feng",
""
],
[
"Sikdar",
"Biplab",
""
],
[
"Lao",
"Yingjie",
""
]
] |
TITLE: UIBDiffusion: Universal Imperceptible Backdoor Attack for Diffusion
Models
ABSTRACT: Recent studies show that diffusion models (DMs) are vulnerable to backdoor
attacks. Existing backdoor attacks impose unconcealed triggers (e.g., a gray
box and eyeglasses) that contain evident patterns, rendering remarkable attack
effects yet easy detection upon human inspection and defensive algorithms.
While it is possible to improve stealthiness by reducing the strength of the
backdoor, doing so can significantly compromise its generality and
effectiveness. In this paper, we propose UIBDiffusion, the universal
imperceptible backdoor attack for diffusion models, which allows us to achieve
superior attack and generation performance while evading state-of-the-art
defenses. We propose a novel trigger generation approach based on universal
adversarial perturbations (UAPs) and reveal that such perturbations, which are
initially devised for fooling pre-trained discriminative models, can be adapted
as potent imperceptible backdoor triggers for DMs. We evaluate UIBDiffusion on
multiple types of DMs with different kinds of samplers across various datasets
and targets. Experimental results demonstrate that UIBDiffusion brings three
advantages: 1) Universality, the imperceptible trigger is universal (i.e.,
image and model agnostic) where a single trigger is effective to any images and
all diffusion models with different samplers; 2) Utility, it achieves
comparable generation quality (e.g., FID) and even better attack success rate
(i.e., ASR) at low poison rates compared to the prior works; and 3)
Undetectability, UIBDiffusion is plausible to human perception and can bypass
Elijah and TERD, the SOTA defenses against backdoors for DMs. We will release
our backdoor triggers and code.
|
no_new_dataset
| 0.942929
|
2412.12693
|
Wenyu Zhang
|
Wenyu Zhang, Wei En Ng, Lixin Ma, Yuwen Wang, Jungqi Zhao, Allison
Koenecke, Boyang Li, Lu Wang
|
SPHERE: Unveiling Spatial Blind Spots in Vision-Language Models Through
Hierarchical Evaluation
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Current vision-language models may grasp basic spatial cues and simple
directions (e.g. left, right, front, back), but struggle with the
multi-dimensional spatial reasoning necessary for human-like understanding and
real-world applications. To address this gap, we develop SPHERE (Spatial
Perception and Hierarchical Evaluation of REasoning), a hierarchical evaluation
framework supported by a new human-annotated dataset. SPHERE systematically
probes models across increasing levels of complexity, from fundamental skills
to multi-skill integration and high-level reasoning that combines spatial,
visual, and logical understanding. Benchmark evaluation of state-of-the-art
models reveals significant deficiencies, especially in reasoning about distance
and proximity, understanding both egocentric and allocentric perspectives, and
applying spatial logic in physical contexts. These findings expose critical
blind spots in existing models and underscore the need for more advanced
spatial reasoning techniques, driving the development of vision-language models
that align more closely with human spatial cognition. The SPHERE benchmark is
available at https://github.com/zwenyu/SPHERE-VLM.
|
[
{
"version": "v1",
"created": "Tue, 17 Dec 2024 09:10:55 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Feb 2025 10:28:00 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 15:14:37 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Zhang",
"Wenyu",
""
],
[
"Ng",
"Wei En",
""
],
[
"Ma",
"Lixin",
""
],
[
"Wang",
"Yuwen",
""
],
[
"Zhao",
"Jungqi",
""
],
[
"Koenecke",
"Allison",
""
],
[
"Li",
"Boyang",
""
],
[
"Wang",
"Lu",
""
]
] |
TITLE: SPHERE: Unveiling Spatial Blind Spots in Vision-Language Models Through
Hierarchical Evaluation
ABSTRACT: Current vision-language models may grasp basic spatial cues and simple
directions (e.g. left, right, front, back), but struggle with the
multi-dimensional spatial reasoning necessary for human-like understanding and
real-world applications. To address this gap, we develop SPHERE (Spatial
Perception and Hierarchical Evaluation of REasoning), a hierarchical evaluation
framework supported by a new human-annotated dataset. SPHERE systematically
probes models across increasing levels of complexity, from fundamental skills
to multi-skill integration and high-level reasoning that combines spatial,
visual, and logical understanding. Benchmark evaluation of state-of-the-art
models reveals significant deficiencies, especially in reasoning about distance
and proximity, understanding both egocentric and allocentric perspectives, and
applying spatial logic in physical contexts. These findings expose critical
blind spots in existing models and underscore the need for more advanced
spatial reasoning techniques, driving the development of vision-language models
that align more closely with human spatial cognition. The SPHERE benchmark is
available at https://github.com/zwenyu/SPHERE-VLM.
|
new_dataset
| 0.957318
|
2412.13211
|
Arth Shukla
|
Arth Shukla, Stone Tao, Hao Su
|
ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home
Rearrangement Tasks
| null | null | null | null |
cs.RO cs.AI cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
High-quality benchmarks are the foundation for embodied AI research, enabling
significant advancements in long-horizon navigation, manipulation and
rearrangement tasks. However, as frontier tasks in robotics get more advanced,
they require faster simulation speed, more intricate test environments, and
larger demonstration datasets. To this end, we present MS-HAB, a holistic
benchmark for low-level manipulation and in-home object rearrangement. First,
we provide a GPU-accelerated implementation of the Home Assistant Benchmark
(HAB). We support realistic low-level control and achieve over 3x the speed of
prior magical grasp implementations at a fraction of the GPU memory usage.
Second, we train extensive reinforcement learning (RL) and imitation learning
(IL) baselines for future work to compare against. Finally, we develop a
rule-based trajectory filtering system to sample specific demonstrations from
our RL policies which match predefined criteria for robot behavior and safety.
Combining demonstration filtering with our fast environments enables efficient,
controlled data generation at scale.
|
[
{
"version": "v1",
"created": "Mon, 9 Dec 2024 01:29:24 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Dec 2024 05:21:39 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 10:10:33 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Shukla",
"Arth",
""
],
[
"Tao",
"Stone",
""
],
[
"Su",
"Hao",
""
]
] |
TITLE: ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home
Rearrangement Tasks
ABSTRACT: High-quality benchmarks are the foundation for embodied AI research, enabling
significant advancements in long-horizon navigation, manipulation and
rearrangement tasks. However, as frontier tasks in robotics get more advanced,
they require faster simulation speed, more intricate test environments, and
larger demonstration datasets. To this end, we present MS-HAB, a holistic
benchmark for low-level manipulation and in-home object rearrangement. First,
we provide a GPU-accelerated implementation of the Home Assistant Benchmark
(HAB). We support realistic low-level control and achieve over 3x the speed of
prior magical grasp implementations at a fraction of the GPU memory usage.
Second, we train extensive reinforcement learning (RL) and imitation learning
(IL) baselines for future work to compare against. Finally, we develop a
rule-based trajectory filtering system to sample specific demonstrations from
our RL policies which match predefined criteria for robot behavior and safety.
Combining demonstration filtering with our fast environments enables efficient,
controlled data generation at scale.
|
no_new_dataset
| 0.940463
|
2412.13299
|
Eichi Takaya
|
Eichi Takaya and Shinnosuke Yamamoto
|
In-context learning for medical image segmentation
| null | null | null | null |
eess.IV cs.AI cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Annotation of medical images, such as MRI and CT scans, is crucial for
evaluating treatment efficacy and planning radiotherapy. However, the extensive
workload of medical professionals limits their ability to annotate large image
datasets, posing a bottleneck for AI applications in medical imaging. To
address this, we propose In-context Cascade Segmentation (ICS), a novel method
that minimizes annotation requirements while achieving high segmentation
accuracy for sequential medical images. ICS builds on the UniverSeg framework,
which performs few-shot segmentation using support images without additional
training. By iteratively adding the inference results of each slice to the
support set, ICS propagates information forward and backward through the
sequence, ensuring inter-slice consistency. We evaluate the proposed method on
the HVSMR dataset, which includes segmentation tasks for eight cardiac regions.
Experimental results demonstrate that ICS significantly improves segmentation
performance in complex anatomical regions, particularly in maintaining boundary
consistency across slices, compared to baseline methods. The study also
highlights the impact of the number and position of initial support slices on
segmentation accuracy. ICS offers a promising solution for reducing annotation
burdens while delivering robust segmentation results, paving the way for its
broader adoption in clinical and research applications.
|
[
{
"version": "v1",
"created": "Tue, 17 Dec 2024 19:59:08 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 06:19:59 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Takaya",
"Eichi",
""
],
[
"Yamamoto",
"Shinnosuke",
""
]
] |
TITLE: In-context learning for medical image segmentation
ABSTRACT: Annotation of medical images, such as MRI and CT scans, is crucial for
evaluating treatment efficacy and planning radiotherapy. However, the extensive
workload of medical professionals limits their ability to annotate large image
datasets, posing a bottleneck for AI applications in medical imaging. To
address this, we propose In-context Cascade Segmentation (ICS), a novel method
that minimizes annotation requirements while achieving high segmentation
accuracy for sequential medical images. ICS builds on the UniverSeg framework,
which performs few-shot segmentation using support images without additional
training. By iteratively adding the inference results of each slice to the
support set, ICS propagates information forward and backward through the
sequence, ensuring inter-slice consistency. We evaluate the proposed method on
the HVSMR dataset, which includes segmentation tasks for eight cardiac regions.
Experimental results demonstrate that ICS significantly improves segmentation
performance in complex anatomical regions, particularly in maintaining boundary
consistency across slices, compared to baseline methods. The study also
highlights the impact of the number and position of initial support slices on
segmentation accuracy. ICS offers a promising solution for reducing annotation
burdens while delivering robust segmentation results, paving the way for its
broader adoption in clinical and research applications.
|
no_new_dataset
| 0.943764
|
2412.14613
|
Nakamasa Inoue
|
Masanari Ohi, Masahiro Kaneko, Naoaki Okazaki, Nakamasa Inoue
|
Multi-modal, Multi-task, Multi-criteria Automatic Evaluation with Vision
Language Models
| null | null | null | null |
cs.CL cs.AI cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision-language models (VLMs) have shown impressive abilities across a range
of multi-modal tasks. However, existing metrics for evaluating the quality of
text generated by VLMs typically focus on an overall evaluation for a specific
task, such as image captioning. While the overall evaluation is essential for
any task, the criteria prioritized can differ depending on the task, making it
challenging for current metrics to adapt to multi-task scenarios. To address
this limitation, we propose HarmonicEval, a reference-free comprehensive
evaluation metric that aggregates criterion-wise scores to produce the overall
score in a bottom-up manner. Furthermore, we construct the Multi-task
Multi-criteria Human Evaluation (MMHE) dataset, which comprises 18,000 expert
human judgments across four multi-modal tasks. Our experiments demonstrate that
HarmonicEval achieves higher correlations with human judgments than
conventional metrics while providing numerical scores for each criterion.
|
[
{
"version": "v1",
"created": "Thu, 19 Dec 2024 08:03:16 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 03:04:05 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Ohi",
"Masanari",
""
],
[
"Kaneko",
"Masahiro",
""
],
[
"Okazaki",
"Naoaki",
""
],
[
"Inoue",
"Nakamasa",
""
]
] |
TITLE: Multi-modal, Multi-task, Multi-criteria Automatic Evaluation with Vision
Language Models
ABSTRACT: Vision-language models (VLMs) have shown impressive abilities across a range
of multi-modal tasks. However, existing metrics for evaluating the quality of
text generated by VLMs typically focus on an overall evaluation for a specific
task, such as image captioning. While the overall evaluation is essential for
any task, the criteria prioritized can differ depending on the task, making it
challenging for current metrics to adapt to multi-task scenarios. To address
this limitation, we propose HarmonicEval, a reference-free comprehensive
evaluation metric that aggregates criterion-wise scores to produce the overall
score in a bottom-up manner. Furthermore, we construct the Multi-task
Multi-criteria Human Evaluation (MMHE) dataset, which comprises 18,000 expert
human judgments across four multi-modal tasks. Our experiments demonstrate that
HarmonicEval achieves higher correlations with human judgments than
conventional metrics while providing numerical scores for each criterion.
|
new_dataset
| 0.958654
|
2412.16100
|
Bishwamittra Ghosh
|
Bishwamittra Ghosh, Sarah Hasan, Naheed Anjum Arafat, Arijit Khan
|
Logical Consistency of Large Language Models in Fact-checking
|
Published at ICLR 2025
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In recent years, large language models (LLMs) have demonstrated significant
success in performing varied natural language tasks such as language
translation, question-answering, summarizing, fact-checking, etc. Despite LLMs'
impressive ability to generate human-like texts, LLMs are infamous for their
inconsistent responses - a meaning-preserving change in the input query results
in an inconsistent response and attributes to vulnerabilities of LLMs such as
hallucination. Consequently, existing research focuses on simple
paraphrasing-based consistency assessment of LLMs, and ignores complex queries
that necessitate an even better understanding of logical reasoning by an LLM.
Our work therefore addresses the logical inconsistency of LLMs under complex
logical queries with primitive logical operators, e.g., negation, conjunction,
and disjunction. As a test bed, we consider retrieval-augmented LLMs on a
fact-checking task involving propositional logic queries from knowledge graphs
(KGs). Our contributions are threefold. Benchmark: We introduce three logical
fact-checking datasets over KGs for community development towards logically
consistent LLMs. Assessment: We propose consistency measures of LLMs on
propositional logic queries and demonstrate that existing LLMs lack logical
consistency, especially on complex queries. Improvement: We employ supervised
fine-tuning to improve the logical consistency of LLMs on the complex
fact-checking task with KG contexts. We have made our source code and
benchmarks available.
|
[
{
"version": "v1",
"created": "Fri, 20 Dec 2024 17:42:25 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 17:02:23 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Ghosh",
"Bishwamittra",
""
],
[
"Hasan",
"Sarah",
""
],
[
"Arafat",
"Naheed Anjum",
""
],
[
"Khan",
"Arijit",
""
]
] |
TITLE: Logical Consistency of Large Language Models in Fact-checking
ABSTRACT: In recent years, large language models (LLMs) have demonstrated significant
success in performing varied natural language tasks such as language
translation, question-answering, summarizing, fact-checking, etc. Despite LLMs'
impressive ability to generate human-like texts, LLMs are infamous for their
inconsistent responses - a meaning-preserving change in the input query results
in an inconsistent response and attributes to vulnerabilities of LLMs such as
hallucination. Consequently, existing research focuses on simple
paraphrasing-based consistency assessment of LLMs, and ignores complex queries
that necessitate an even better understanding of logical reasoning by an LLM.
Our work therefore addresses the logical inconsistency of LLMs under complex
logical queries with primitive logical operators, e.g., negation, conjunction,
and disjunction. As a test bed, we consider retrieval-augmented LLMs on a
fact-checking task involving propositional logic queries from knowledge graphs
(KGs). Our contributions are threefold. Benchmark: We introduce three logical
fact-checking datasets over KGs for community development towards logically
consistent LLMs. Assessment: We propose consistency measures of LLMs on
propositional logic queries and demonstrate that existing LLMs lack logical
consistency, especially on complex queries. Improvement: We employ supervised
fine-tuning to improve the logical consistency of LLMs on the complex
fact-checking task with KG contexts. We have made our source code and
benchmarks available.
|
new_dataset
| 0.960287
|
2501.04903
|
Nathan Phelps
|
Nathan Phelps, Daniel J. Lizotte, and Douglas G. Woolford
|
Towards understanding the bias in decision trees
| null | null | null | null |
stat.ML cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
There is a widespread and longstanding belief that machine learning models
are biased towards the majority (or negative) class when learning from
imbalanced data, leading them to neglect or ignore the minority (or positive)
class. In this study, we show that this belief is not necessarily correct for
decision trees, and that their bias can actually be in the opposite direction.
Motivated by a recent simulation study that suggested that decision trees can
be biased towards the minority class, our paper aims to reconcile the conflict
between that study and decades of other works. First, we critically evaluate
past literature on this problem, finding that failing to consider the data
generating process has led to incorrect conclusions about the bias in decision
trees. We then prove that, under specific conditions related to the predictors,
decision trees fit to purity and trained on a dataset with only one positive
case are biased towards the minority class. Finally, we demonstrate that splits
in a decision tree are also biased when there is more than one positive case.
Our findings have implications on the use of popular tree-based models, such as
random forests.
|
[
{
"version": "v1",
"created": "Thu, 9 Jan 2025 01:31:30 GMT"
},
{
"version": "v2",
"created": "Mon, 27 Jan 2025 18:22:59 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 14:03:56 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Phelps",
"Nathan",
""
],
[
"Lizotte",
"Daniel J.",
""
],
[
"Woolford",
"Douglas G.",
""
]
] |
TITLE: Towards understanding the bias in decision trees
ABSTRACT: There is a widespread and longstanding belief that machine learning models
are biased towards the majority (or negative) class when learning from
imbalanced data, leading them to neglect or ignore the minority (or positive)
class. In this study, we show that this belief is not necessarily correct for
decision trees, and that their bias can actually be in the opposite direction.
Motivated by a recent simulation study that suggested that decision trees can
be biased towards the minority class, our paper aims to reconcile the conflict
between that study and decades of other works. First, we critically evaluate
past literature on this problem, finding that failing to consider the data
generating process has led to incorrect conclusions about the bias in decision
trees. We then prove that, under specific conditions related to the predictors,
decision trees fit to purity and trained on a dataset with only one positive
case are biased towards the minority class. Finally, we demonstrate that splits
in a decision tree are also biased when there is more than one positive case.
Our findings have implications on the use of popular tree-based models, such as
random forests.
|
no_new_dataset
| 0.94625
|
2501.06842
|
Tianjin Huang
|
Tianjin Huang, Ziquan Zhu, Gaojie Jin, Lu Liu, Zhangyang Wang, Shiwei
Liu
|
SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training
| null | null | null | null |
cs.LG cs.AI cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Large Language Models (LLMs) have demonstrated exceptional performance across
diverse tasks, yet their training remains highly resource-intensive and
susceptible to critical challenges such as training instability. A predominant
source of this instability stems from gradient and loss spikes, which disrupt
the learning process, often leading to costly interventions like checkpoint
recovery and experiment restarts, further amplifying inefficiencies. This paper
presents a comprehensive investigation into gradient spikes observed during LLM
training, revealing their prevalence across multiple architectures and
datasets. Our analysis shows that these spikes can be up to $1000\times$ larger
than typical gradients, substantially deteriorating model performance. To
address this issue, we propose Spike-Aware Adam with Momentum Reset SPAM, a
novel optimizer designed to counteract gradient spikes through momentum reset
and spike-aware gradient clipping. Extensive experiments, including both
pre-training and fine-tuning, demonstrate that SPAM consistently surpasses Adam
and its variants across various tasks, including (1) LLM pre-training from 60M
to 1B, (2) 4-bit LLM pre-training,(3) reinforcement learning, and (4) Time
Series Forecasting. Additionally, SPAM facilitates memory-efficient training by
enabling sparse momentum, where only a subset of momentum terms are maintained
and updated. When operating under memory constraints, SPAM outperforms
state-of-the-art memory-efficient optimizers such as GaLore and Adam-Mini. Our
work underscores the importance of mitigating gradient spikes in LLM training
and introduces an effective optimization strategy that enhances both training
stability and resource efficiency at scale. Code is available at
https://github.com/TianjinYellow/SPAM-Optimizer.git
|
[
{
"version": "v1",
"created": "Sun, 12 Jan 2025 15:21:22 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 15:15:31 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Huang",
"Tianjin",
""
],
[
"Zhu",
"Ziquan",
""
],
[
"Jin",
"Gaojie",
""
],
[
"Liu",
"Lu",
""
],
[
"Wang",
"Zhangyang",
""
],
[
"Liu",
"Shiwei",
""
]
] |
TITLE: SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training
ABSTRACT: Large Language Models (LLMs) have demonstrated exceptional performance across
diverse tasks, yet their training remains highly resource-intensive and
susceptible to critical challenges such as training instability. A predominant
source of this instability stems from gradient and loss spikes, which disrupt
the learning process, often leading to costly interventions like checkpoint
recovery and experiment restarts, further amplifying inefficiencies. This paper
presents a comprehensive investigation into gradient spikes observed during LLM
training, revealing their prevalence across multiple architectures and
datasets. Our analysis shows that these spikes can be up to $1000\times$ larger
than typical gradients, substantially deteriorating model performance. To
address this issue, we propose Spike-Aware Adam with Momentum Reset SPAM, a
novel optimizer designed to counteract gradient spikes through momentum reset
and spike-aware gradient clipping. Extensive experiments, including both
pre-training and fine-tuning, demonstrate that SPAM consistently surpasses Adam
and its variants across various tasks, including (1) LLM pre-training from 60M
to 1B, (2) 4-bit LLM pre-training,(3) reinforcement learning, and (4) Time
Series Forecasting. Additionally, SPAM facilitates memory-efficient training by
enabling sparse momentum, where only a subset of momentum terms are maintained
and updated. When operating under memory constraints, SPAM outperforms
state-of-the-art memory-efficient optimizers such as GaLore and Adam-Mini. Our
work underscores the importance of mitigating gradient spikes in LLM training
and introduces an effective optimization strategy that enhances both training
stability and resource efficiency at scale. Code is available at
https://github.com/TianjinYellow/SPAM-Optimizer.git
|
no_new_dataset
| 0.947137
|
2501.09768
|
Mohamed Bayan Kmainasi
|
Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Amani Al-Ghraibah
|
Can Large Language Models Predict the Outcome of Judicial Decisions?
| null | null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Large Language Models (LLMs) have shown exceptional capabilities in Natural
Language Processing (NLP) across diverse domains. However, their application in
specialized tasks such as Legal Judgment Prediction (LJP) for low-resource
languages like Arabic remains underexplored. In this work, we address this gap
by developing an Arabic LJP dataset, collected and preprocessed from Saudi
commercial court judgments. We benchmark state-of-the-art open-source LLMs,
including LLaMA-3.2-3B and LLaMA-3.1-8B, under varying configurations such as
zero-shot, one-shot, and fine-tuning using LoRA. Additionally, we employed a
comprehensive evaluation framework that integrates both quantitative metrics
(such as BLEU, ROUGE, and BERT) and qualitative assessments (including
Coherence, Legal Language, Clarity, etc.) using an LLM. Our results demonstrate
that fine-tuned smaller models achieve comparable performance to larger models
in task-specific contexts while offering significant resource efficiency.
Furthermore, we investigate the impact of fine-tuning the model on a diverse
set of instructions, offering valuable insights into the development of a more
human-centric and adaptable LLM. We have made the dataset, code, and models
publicly available to provide a solid foundation for future research in Arabic
legal NLP.
|
[
{
"version": "v1",
"created": "Wed, 15 Jan 2025 11:32:35 GMT"
},
{
"version": "v2",
"created": "Wed, 5 Feb 2025 12:17:36 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 18:27:21 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Kmainasi",
"Mohamed Bayan",
""
],
[
"Shahroor",
"Ali Ezzat",
""
],
[
"Al-Ghraibah",
"Amani",
""
]
] |
TITLE: Can Large Language Models Predict the Outcome of Judicial Decisions?
ABSTRACT: Large Language Models (LLMs) have shown exceptional capabilities in Natural
Language Processing (NLP) across diverse domains. However, their application in
specialized tasks such as Legal Judgment Prediction (LJP) for low-resource
languages like Arabic remains underexplored. In this work, we address this gap
by developing an Arabic LJP dataset, collected and preprocessed from Saudi
commercial court judgments. We benchmark state-of-the-art open-source LLMs,
including LLaMA-3.2-3B and LLaMA-3.1-8B, under varying configurations such as
zero-shot, one-shot, and fine-tuning using LoRA. Additionally, we employed a
comprehensive evaluation framework that integrates both quantitative metrics
(such as BLEU, ROUGE, and BERT) and qualitative assessments (including
Coherence, Legal Language, Clarity, etc.) using an LLM. Our results demonstrate
that fine-tuned smaller models achieve comparable performance to larger models
in task-specific contexts while offering significant resource efficiency.
Furthermore, we investigate the impact of fine-tuning the model on a diverse
set of instructions, offering valuable insights into the development of a more
human-centric and adaptable LLM. We have made the dataset, code, and models
publicly available to provide a solid foundation for future research in Arabic
legal NLP.
|
new_dataset
| 0.955858
|
2501.12087
|
Branislava Jankovic
|
Branislava Jankovic, Sabina Jangirova, Waseem Ullah, Latif U. Khan,
Mohsen Guizani
|
UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer
Model
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dangerous surroundings and difficult-to-reach landscapes introduce
significant complications for adequate disaster management and recuperation.
These problems can be solved by engaging unmanned aerial vehicles (UAVs)
provided with embedded platforms and optical sensors. In this work, we focus on
enabling onboard aerial image processing to ensure proper and real-time
disaster detection. Such a setting usually causes challenges due to the limited
hardware resources of UAVs. However, privacy, connectivity, and latency issues
can be avoided. We suggest a UAV-assisted edge framework for disaster
detection, leveraging our proposed model optimized for onboard real-time aerial
image classification. The optimization of the model is achieved using
post-training quantization techniques. To address the limited number of
disaster cases in existing benchmark datasets and therefore ensure real-world
adoption of our model, we construct a novel dataset, DisasterEye, featuring
disaster scenes captured by UAVs and individuals on-site. Experimental results
reveal the efficacy of our model, reaching high accuracy with lowered inference
latency and memory use on both traditional machines and resource-limited
devices. This shows that the scalability and adaptability of our method make it
a powerful solution for real-time disaster management on resource-constrained
UAV platforms.
|
[
{
"version": "v1",
"created": "Tue, 21 Jan 2025 12:29:45 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 10:42:30 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Jankovic",
"Branislava",
""
],
[
"Jangirova",
"Sabina",
""
],
[
"Ullah",
"Waseem",
""
],
[
"Khan",
"Latif U.",
""
],
[
"Guizani",
"Mohsen",
""
]
] |
TITLE: UAV-Assisted Real-Time Disaster Detection Using Optimized Transformer
Model
ABSTRACT: Dangerous surroundings and difficult-to-reach landscapes introduce
significant complications for adequate disaster management and recuperation.
These problems can be solved by engaging unmanned aerial vehicles (UAVs)
provided with embedded platforms and optical sensors. In this work, we focus on
enabling onboard aerial image processing to ensure proper and real-time
disaster detection. Such a setting usually causes challenges due to the limited
hardware resources of UAVs. However, privacy, connectivity, and latency issues
can be avoided. We suggest a UAV-assisted edge framework for disaster
detection, leveraging our proposed model optimized for onboard real-time aerial
image classification. The optimization of the model is achieved using
post-training quantization techniques. To address the limited number of
disaster cases in existing benchmark datasets and therefore ensure real-world
adoption of our model, we construct a novel dataset, DisasterEye, featuring
disaster scenes captured by UAVs and individuals on-site. Experimental results
reveal the efficacy of our model, reaching high accuracy with lowered inference
latency and memory use on both traditional machines and resource-limited
devices. This shows that the scalability and adaptability of our method make it
a powerful solution for real-time disaster management on resource-constrained
UAV platforms.
|
new_dataset
| 0.959345
|
2501.15089
|
Zhan Ling
|
Zhan Ling, Kang Liu, Kai Yan, Yifan Yang, Weijian Lin, Ting-Han Fan,
Lingfeng Shen, Zhengyin Du, Jiecao Chen
|
LongReason: A Synthetic Long-Context Reasoning Benchmark via Context
Expansion
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Large language models (LLMs) have demonstrated remarkable progress in
understanding long-context inputs. However, benchmarks for evaluating the
long-context reasoning abilities of LLMs fall behind the pace. Existing
benchmarks often focus on a narrow range of tasks or those that do not demand
complex reasoning. To address this gap and enable a more comprehensive
evaluation of the long-context reasoning capabilities of current LLMs, we
propose a new synthetic benchmark, LongReason, which is constructed by
synthesizing long-context reasoning questions from a varied set of
short-context reasoning questions through context expansion. LongReason
consists of 794 multiple-choice reasoning questions with diverse reasoning
patterns across three task categories: reading comprehension, logical
inference, and mathematical word problems. We evaluate 21 LLMs on LongReason,
revealing that most models experience significant performance drops as context
length increases. Our further analysis shows that even state-of-the-art LLMs
still have significant room for improvement in providing robust reasoning
across different tasks. We have open-sourced LongReason under
https://huggingface.co/datasets/lz1bytedance/LongReason to support the
comprehensive evaluation of LLMs' long-context reasoning capabilities.
|
[
{
"version": "v1",
"created": "Sat, 25 Jan 2025 05:32:14 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 07:53:20 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Ling",
"Zhan",
""
],
[
"Liu",
"Kang",
""
],
[
"Yan",
"Kai",
""
],
[
"Yang",
"Yifan",
""
],
[
"Lin",
"Weijian",
""
],
[
"Fan",
"Ting-Han",
""
],
[
"Shen",
"Lingfeng",
""
],
[
"Du",
"Zhengyin",
""
],
[
"Chen",
"Jiecao",
""
]
] |
TITLE: LongReason: A Synthetic Long-Context Reasoning Benchmark via Context
Expansion
ABSTRACT: Large language models (LLMs) have demonstrated remarkable progress in
understanding long-context inputs. However, benchmarks for evaluating the
long-context reasoning abilities of LLMs fall behind the pace. Existing
benchmarks often focus on a narrow range of tasks or those that do not demand
complex reasoning. To address this gap and enable a more comprehensive
evaluation of the long-context reasoning capabilities of current LLMs, we
propose a new synthetic benchmark, LongReason, which is constructed by
synthesizing long-context reasoning questions from a varied set of
short-context reasoning questions through context expansion. LongReason
consists of 794 multiple-choice reasoning questions with diverse reasoning
patterns across three task categories: reading comprehension, logical
inference, and mathematical word problems. We evaluate 21 LLMs on LongReason,
revealing that most models experience significant performance drops as context
length increases. Our further analysis shows that even state-of-the-art LLMs
still have significant room for improvement in providing robust reasoning
across different tasks. We have open-sourced LongReason under
https://huggingface.co/datasets/lz1bytedance/LongReason to support the
comprehensive evaluation of LLMs' long-context reasoning capabilities.
|
new_dataset
| 0.964288
|
2501.15296
|
Ayan Sengupta
|
Ayan Sengupta, Siddhant Chaudhary, Tanmoy Chakraborty
|
You Only Prune Once: Designing Calibration-Free Model Compression With
Policy Learning
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The ever-increasing size of large language models (LLMs) presents significant
challenges for deployment due to their heavy computational and memory
requirements. Current model pruning techniques attempt to alleviate these
issues by relying heavily on external calibration datasets to determine which
parameters to prune or compress, thus limiting their flexibility and
scalability across different compression ratios. Moreover, these methods often
cause severe performance degradation, particularly in downstream tasks, when
subjected to higher compression rates. In this paper, we propose PruneNet, a
novel model compression method that addresses these limitations by
reformulating model pruning as a policy learning process. PruneNet decouples
the pruning process from the model architecture, eliminating the need for
calibration datasets. It learns a stochastic pruning policy to assess parameter
importance solely based on intrinsic model properties while preserving the
spectral structure to minimize information loss. PruneNet can compress the
LLaMA-2-7B model in just 15 minutes, achieving over 80% retention of its
zero-shot performance with a 30% compression ratio, outperforming existing
methods that retain only 75% performance. Furthermore, on complex multitask
language understanding tasks, PruneNet demonstrates its robustness by
preserving up to 80% performance of the original model, proving itself a
superior alternative to conventional structured compression techniques.
|
[
{
"version": "v1",
"created": "Sat, 25 Jan 2025 18:26:39 GMT"
},
{
"version": "v2",
"created": "Wed, 19 Feb 2025 06:34:23 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 15:23:40 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Sengupta",
"Ayan",
""
],
[
"Chaudhary",
"Siddhant",
""
],
[
"Chakraborty",
"Tanmoy",
""
]
] |
TITLE: You Only Prune Once: Designing Calibration-Free Model Compression With
Policy Learning
ABSTRACT: The ever-increasing size of large language models (LLMs) presents significant
challenges for deployment due to their heavy computational and memory
requirements. Current model pruning techniques attempt to alleviate these
issues by relying heavily on external calibration datasets to determine which
parameters to prune or compress, thus limiting their flexibility and
scalability across different compression ratios. Moreover, these methods often
cause severe performance degradation, particularly in downstream tasks, when
subjected to higher compression rates. In this paper, we propose PruneNet, a
novel model compression method that addresses these limitations by
reformulating model pruning as a policy learning process. PruneNet decouples
the pruning process from the model architecture, eliminating the need for
calibration datasets. It learns a stochastic pruning policy to assess parameter
importance solely based on intrinsic model properties while preserving the
spectral structure to minimize information loss. PruneNet can compress the
LLaMA-2-7B model in just 15 minutes, achieving over 80% retention of its
zero-shot performance with a 30% compression ratio, outperforming existing
methods that retain only 75% performance. Furthermore, on complex multitask
language understanding tasks, PruneNet demonstrates its robustness by
preserving up to 80% performance of the original model, proving itself a
superior alternative to conventional structured compression techniques.
|
no_new_dataset
| 0.944177
|
2501.15889
|
Federico Errica
|
Federico Errica, Henrik Christiansen, Viktor Zaverkin, Mathias
Niepert, Francesco Alesiani
|
Adaptive Width Neural Networks
| null | null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For almost 70 years, researchers have mostly relied on hyper-parameter tuning
to pick the width of neural networks' layers out of many possible choices. This
paper challenges the status quo by introducing an easy-to-use technique to
learn an unbounded width of a neural network's layer during training. The
technique does not rely on alternate optimization nor hand-crafted gradient
heuristics; rather, it jointly optimizes the width and the parameters of each
layer via simple backpropagation. We apply the technique to a broad range of
data domains such as tables, images, texts, and graphs, showing how the width
adapts to the task's difficulty. By imposing a soft ordering of importance
among neurons, it is possible to truncate the trained network at virtually zero
cost, achieving a smooth trade-off between performance and compute resources in
a structured way. Alternatively, one can dynamically compress the network with
no performance degradation. In light of recent foundation models trained on
large datasets, believed to require billions of parameters and where
hyper-parameter tuning is unfeasible due to huge training costs, our approach
stands as a viable alternative for width learning.
|
[
{
"version": "v1",
"created": "Mon, 27 Jan 2025 09:25:56 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 08:28:08 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Errica",
"Federico",
""
],
[
"Christiansen",
"Henrik",
""
],
[
"Zaverkin",
"Viktor",
""
],
[
"Niepert",
"Mathias",
""
],
[
"Alesiani",
"Francesco",
""
]
] |
TITLE: Adaptive Width Neural Networks
ABSTRACT: For almost 70 years, researchers have mostly relied on hyper-parameter tuning
to pick the width of neural networks' layers out of many possible choices. This
paper challenges the status quo by introducing an easy-to-use technique to
learn an unbounded width of a neural network's layer during training. The
technique does not rely on alternate optimization nor hand-crafted gradient
heuristics; rather, it jointly optimizes the width and the parameters of each
layer via simple backpropagation. We apply the technique to a broad range of
data domains such as tables, images, texts, and graphs, showing how the width
adapts to the task's difficulty. By imposing a soft ordering of importance
among neurons, it is possible to truncate the trained network at virtually zero
cost, achieving a smooth trade-off between performance and compute resources in
a structured way. Alternatively, one can dynamically compress the network with
no performance degradation. In light of recent foundation models trained on
large datasets, believed to require billions of parameters and where
hyper-parameter tuning is unfeasible due to huge training costs, our approach
stands as a viable alternative for width learning.
|
no_new_dataset
| 0.946151
|
2501.16239
|
Antoine Olivier
|
Alexandre Filiot, Nicolas Dop, Oussama Tchita, Auriane Riou, R\'emy
Dubois, Thomas Peeters, Daria Valter, Marin Scalbert, Charlie Saillard,
Genevi\`eve Robin, Antoine Olivier
|
Distilling foundation models for robust and efficient models in digital
pathology
|
Preprint
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In recent years, the advent of foundation models (FM) for digital pathology
has relied heavily on scaling the pre-training datasets and the model size,
yielding large and powerful models. While it resulted in improving the
performance on diverse downstream tasks, it also introduced increased
computational cost and inference time. In this work, we explore the
distillation of a large foundation model into a smaller one, reducing the
number of parameters by several orders of magnitude. Leveraging distillation
techniques, our distilled model, H0-mini, achieves nearly comparable
performance to large FMs at a significantly reduced inference cost. It is
evaluated on several public benchmarks, achieving 3rd place on the HEST
benchmark and 5th place on the EVA benchmark. Additionally, a robustness
analysis conducted on the PLISM dataset demonstrates that our distilled model
reaches excellent robustness to variations in staining and scanning conditions,
significantly outperforming other state-of-the art models. This opens new
perspectives to design lightweight and robust models for digital pathology,
without compromising on performance.
|
[
{
"version": "v1",
"created": "Mon, 27 Jan 2025 17:35:39 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Jan 2025 17:09:41 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 15:44:24 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Filiot",
"Alexandre",
""
],
[
"Dop",
"Nicolas",
""
],
[
"Tchita",
"Oussama",
""
],
[
"Riou",
"Auriane",
""
],
[
"Dubois",
"Rémy",
""
],
[
"Peeters",
"Thomas",
""
],
[
"Valter",
"Daria",
""
],
[
"Scalbert",
"Marin",
""
],
[
"Saillard",
"Charlie",
""
],
[
"Robin",
"Geneviève",
""
],
[
"Olivier",
"Antoine",
""
]
] |
TITLE: Distilling foundation models for robust and efficient models in digital
pathology
ABSTRACT: In recent years, the advent of foundation models (FM) for digital pathology
has relied heavily on scaling the pre-training datasets and the model size,
yielding large and powerful models. While it resulted in improving the
performance on diverse downstream tasks, it also introduced increased
computational cost and inference time. In this work, we explore the
distillation of a large foundation model into a smaller one, reducing the
number of parameters by several orders of magnitude. Leveraging distillation
techniques, our distilled model, H0-mini, achieves nearly comparable
performance to large FMs at a significantly reduced inference cost. It is
evaluated on several public benchmarks, achieving 3rd place on the HEST
benchmark and 5th place on the EVA benchmark. Additionally, a robustness
analysis conducted on the PLISM dataset demonstrates that our distilled model
reaches excellent robustness to variations in staining and scanning conditions,
significantly outperforming other state-of-the art models. This opens new
perspectives to design lightweight and robust models for digital pathology,
without compromising on performance.
|
no_new_dataset
| 0.948632
|
2502.01674
|
Akhilbaran Ghosh
|
Priyam Ganguly, Akhilbaran Ghosh
|
Efficient Brain Tumor Classification with Lightweight CNN Architecture:
A Novel Approach
|
Accepted in FMLDS 2024
|
2024 IEEE International Conference on Future Machine Learning and
Data Science (FMLDS)
|
10.1109/FMLDS63805.2024.00065
| null |
eess.IV cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Brain tumor classification using MRI images is critical in medical
diagnostics, where early and accurate detection significantly impacts patient
outcomes. While recent advancements in deep learning (DL), particularly CNNs,
have shown promise, many models struggle with balancing accuracy and
computational efficiency and often lack robustness across diverse datasets. To
address these challenges, we propose a novel model architecture integrating
separable convolutions and squeeze and excitation (SE) blocks, designed to
enhance feature extraction while maintaining computational efficiency. Our
model further incorporates batch normalization and dropout to prevent
overfitting, ensuring stable and reliable performance. The proposed model is
lightweight because it uses separable convolutions, which reduce the number of
parameters, and incorporates global average pooling instead of fully connected
layers to minimize computational complexity while maintaining high accuracy.
Our model does better than other models by about 0.5% to 1.0% in accuracy and
1.5% to 2.5% in loss reduction, as shown by many experiments. It has a
validation accuracy of 99.22% and a test accuracy of 98.44%. These results
highlight the model's ability to generalize effectively across different brain
tumour types, offering a robust tools for clinical applications. Our work sets
a new benchmark in the field, providing a foundation for future research in
optimizing the accuracy and efficiency of DL models for medical image analysis.
|
[
{
"version": "v1",
"created": "Sat, 1 Feb 2025 21:06:42 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Ganguly",
"Priyam",
""
],
[
"Ghosh",
"Akhilbaran",
""
]
] |
TITLE: Efficient Brain Tumor Classification with Lightweight CNN Architecture:
A Novel Approach
ABSTRACT: Brain tumor classification using MRI images is critical in medical
diagnostics, where early and accurate detection significantly impacts patient
outcomes. While recent advancements in deep learning (DL), particularly CNNs,
have shown promise, many models struggle with balancing accuracy and
computational efficiency and often lack robustness across diverse datasets. To
address these challenges, we propose a novel model architecture integrating
separable convolutions and squeeze and excitation (SE) blocks, designed to
enhance feature extraction while maintaining computational efficiency. Our
model further incorporates batch normalization and dropout to prevent
overfitting, ensuring stable and reliable performance. The proposed model is
lightweight because it uses separable convolutions, which reduce the number of
parameters, and incorporates global average pooling instead of fully connected
layers to minimize computational complexity while maintaining high accuracy.
Our model does better than other models by about 0.5% to 1.0% in accuracy and
1.5% to 2.5% in loss reduction, as shown by many experiments. It has a
validation accuracy of 99.22% and a test accuracy of 98.44%. These results
highlight the model's ability to generalize effectively across different brain
tumour types, offering a robust tools for clinical applications. Our work sets
a new benchmark in the field, providing a foundation for future research in
optimizing the accuracy and efficiency of DL models for medical image analysis.
|
no_new_dataset
| 0.949059
|
2502.06136
|
Sagar Barad
|
Rucha Bhalchandra Joshi, Sagar Prakash Barad, Nidhi Tiwari and
Subhankar Mishra
|
Graph Neural Networks at a Fraction
|
12 pages, 2 figures, accepted at PAKDD 2025
| null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Graph Neural Networks (GNNs) have emerged as powerful tools for learning
representations of graph-structured data. In addition to real-valued GNNs,
quaternion GNNs also perform well on tasks on graph-structured data. With the
aim of reducing the energy footprint, we reduce the model size while
maintaining accuracy comparable to that of the original-sized GNNs. This paper
introduces Quaternion Message Passing Neural Networks (QMPNNs), a framework
that leverages quaternion space to compute node representations. Our approach
offers a generalizable method for incorporating quaternion representations into
GNN architectures at one-fourth of the original parameter count. Furthermore,
we present a novel perspective on Graph Lottery Tickets, redefining their
applicability within the context of GNNs and QMPNNs. We specifically aim to
find the initialization lottery from the subnetwork of the GNNs that can
achieve comparable performance to the original GNN upon training. Thereby
reducing the trainable model parameters even further. To validate the
effectiveness of our proposed QMPNN framework and LTH for both GNNs and QMPNNs,
we evaluate their performance on real-world datasets across three fundamental
graph-based tasks: node classification, link prediction, and graph
classification.
|
[
{
"version": "v1",
"created": "Mon, 10 Feb 2025 03:55:09 GMT"
},
{
"version": "v2",
"created": "Tue, 11 Feb 2025 06:30:25 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 06:26:53 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Joshi",
"Rucha Bhalchandra",
""
],
[
"Barad",
"Sagar Prakash",
""
],
[
"Tiwari",
"Nidhi",
""
],
[
"Mishra",
"Subhankar",
""
]
] |
TITLE: Graph Neural Networks at a Fraction
ABSTRACT: Graph Neural Networks (GNNs) have emerged as powerful tools for learning
representations of graph-structured data. In addition to real-valued GNNs,
quaternion GNNs also perform well on tasks on graph-structured data. With the
aim of reducing the energy footprint, we reduce the model size while
maintaining accuracy comparable to that of the original-sized GNNs. This paper
introduces Quaternion Message Passing Neural Networks (QMPNNs), a framework
that leverages quaternion space to compute node representations. Our approach
offers a generalizable method for incorporating quaternion representations into
GNN architectures at one-fourth of the original parameter count. Furthermore,
we present a novel perspective on Graph Lottery Tickets, redefining their
applicability within the context of GNNs and QMPNNs. We specifically aim to
find the initialization lottery from the subnetwork of the GNNs that can
achieve comparable performance to the original GNN upon training. Thereby
reducing the trainable model parameters even further. To validate the
effectiveness of our proposed QMPNN framework and LTH for both GNNs and QMPNNs,
we evaluate their performance on real-world datasets across three fundamental
graph-based tasks: node classification, link prediction, and graph
classification.
|
no_new_dataset
| 0.950595
|
2502.07138
|
Girish A. Koushik
|
Girish A. Koushik, Diptesh Kanojia, Helen Treharne
|
Towards a Robust Framework for Multimodal Hate Detection: A Study on
Video vs. Image-based Content
|
Accepted to the MM4SG Workshop at the WebConf 2025
|
Companion Proceedings of the ACM Web Conference 2025 (WWW
Companion '25), April 28-May 2, 2025, Sydney, NSW, Australia
|
10.1145/3701716.3718382
|
979-8-4007-1331-6/2025/04
|
cs.CV cs.CL cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Social media platforms enable the propagation of hateful content across
different modalities such as textual, auditory, and visual, necessitating
effective detection methods. While recent approaches have shown promise in
handling individual modalities, their effectiveness across different modality
combinations remains unexplored. This paper presents a systematic analysis of
fusion-based approaches for multimodal hate detection, focusing on their
performance across video and image-based content. Our comprehensive evaluation
reveals significant modality-specific limitations: while simple embedding
fusion achieves state-of-the-art performance on video content (HateMM dataset)
with a 9.9% points F1-score improvement, it struggles with complex image-text
relationships in memes (Hateful Memes dataset). Through detailed ablation
studies and error analysis, we demonstrate how current fusion approaches fail
to capture nuanced cross-modal interactions, particularly in cases involving
benign confounders. Our findings provide crucial insights for developing more
robust hate detection systems and highlight the need for modality-specific
architectural considerations. The code is available at
https://github.com/gak97/Video-vs-Meme-Hate.
|
[
{
"version": "v1",
"created": "Tue, 11 Feb 2025 00:07:40 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Koushik",
"Girish A.",
""
],
[
"Kanojia",
"Diptesh",
""
],
[
"Treharne",
"Helen",
""
]
] |
TITLE: Towards a Robust Framework for Multimodal Hate Detection: A Study on
Video vs. Image-based Content
ABSTRACT: Social media platforms enable the propagation of hateful content across
different modalities such as textual, auditory, and visual, necessitating
effective detection methods. While recent approaches have shown promise in
handling individual modalities, their effectiveness across different modality
combinations remains unexplored. This paper presents a systematic analysis of
fusion-based approaches for multimodal hate detection, focusing on their
performance across video and image-based content. Our comprehensive evaluation
reveals significant modality-specific limitations: while simple embedding
fusion achieves state-of-the-art performance on video content (HateMM dataset)
with a 9.9% points F1-score improvement, it struggles with complex image-text
relationships in memes (Hateful Memes dataset). Through detailed ablation
studies and error analysis, we demonstrate how current fusion approaches fail
to capture nuanced cross-modal interactions, particularly in cases involving
benign confounders. Our findings provide crucial insights for developing more
robust hate detection systems and highlight the need for modality-specific
architectural considerations. The code is available at
https://github.com/gak97/Video-vs-Meme-Hate.
|
no_new_dataset
| 0.944995
|
2502.10636
|
Hamed Rahimi
|
Hamed Rahimi, Adil Bahaj, Mouad Abrini, Mahdi Khoramshahi, Mounir
Ghogho, Mohamed Chetouani
|
USER-VLM 360: Personalized Vision Language Models with User-aware Tuning
for Social Human-Robot Interactions
| null | null | null | null |
cs.AI cs.HC cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The integration of vision-language models into robotic systems constitutes a
significant advancement in enabling machines to interact with their
surroundings in a more intuitive manner. While VLMs offer rich multimodal
reasoning, existing approaches lack user-specific adaptability, often relying
on generic interaction paradigms that fail to account for individual
behavioral, contextual, or socio-emotional nuances. When customization is
attempted, ethical concerns arise from unmitigated biases in user data, risking
exclusion or unfair treatment. To address these dual challenges, we propose
User-VLM 360{\deg}, a holistic framework integrating multimodal user modeling
with bias-aware optimization. Our approach features: (1) user-aware tuning that
adapts interactions in real time using visual-linguistic signals; (2) bias
mitigation via preference optimization; and (3) curated 360{\deg} socio-emotive
interaction datasets annotated with demographic, emotion, and relational
metadata. Evaluations across eight benchmarks demonstrate state-of-the-art
results: +35.3% F1 in personalized VQA, +47.5% F1 in facial features
understanding, 15% bias reduction, and 30X speedup over baselines. Ablation
studies confirm component efficacy, and deployment on the Pepper robot
validates real-time adaptability across diverse users. We open-source
parameter-efficient 3B/10B models and an ethical verification framework for
responsible adaptation.
|
[
{
"version": "v1",
"created": "Sat, 15 Feb 2025 02:25:49 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 09:38:19 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Rahimi",
"Hamed",
""
],
[
"Bahaj",
"Adil",
""
],
[
"Abrini",
"Mouad",
""
],
[
"Khoramshahi",
"Mahdi",
""
],
[
"Ghogho",
"Mounir",
""
],
[
"Chetouani",
"Mohamed",
""
]
] |
TITLE: USER-VLM 360: Personalized Vision Language Models with User-aware Tuning
for Social Human-Robot Interactions
ABSTRACT: The integration of vision-language models into robotic systems constitutes a
significant advancement in enabling machines to interact with their
surroundings in a more intuitive manner. While VLMs offer rich multimodal
reasoning, existing approaches lack user-specific adaptability, often relying
on generic interaction paradigms that fail to account for individual
behavioral, contextual, or socio-emotional nuances. When customization is
attempted, ethical concerns arise from unmitigated biases in user data, risking
exclusion or unfair treatment. To address these dual challenges, we propose
User-VLM 360{\deg}, a holistic framework integrating multimodal user modeling
with bias-aware optimization. Our approach features: (1) user-aware tuning that
adapts interactions in real time using visual-linguistic signals; (2) bias
mitigation via preference optimization; and (3) curated 360{\deg} socio-emotive
interaction datasets annotated with demographic, emotion, and relational
metadata. Evaluations across eight benchmarks demonstrate state-of-the-art
results: +35.3% F1 in personalized VQA, +47.5% F1 in facial features
understanding, 15% bias reduction, and 30X speedup over baselines. Ablation
studies confirm component efficacy, and deployment on the Pepper robot
validates real-time adaptability across diverse users. We open-source
parameter-efficient 3B/10B models and an ethical verification framework for
responsible adaptation.
|
no_new_dataset
| 0.942981
|
2502.11037
|
Xin Gao
|
Xin Gao, Jian Pu
|
Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs
|
10 pages, 4 figures, ICLR 2025
| null | null | null |
cs.LG cs.AI cs.CV
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Multi-View Representation Learning (MVRL) aims to derive a unified
representation from multi-view data by leveraging shared and complementary
information across views. However, when views are irregularly missing, the
incomplete data can lead to representations that lack sufficiency and
consistency. To address this, we propose Multi-View Permutation of Variational
Auto-Encoders (MVP), which excavates invariant relationships between views in
incomplete data. MVP establishes inter-view correspondences in the latent space
of Variational Auto-Encoders, enabling the inference of missing views and the
aggregation of more sufficient information. To derive a valid Evidence Lower
Bound (ELBO) for learning, we apply permutations to randomly reorder variables
for cross-view generation and then partition them by views to maintain
invariant meanings under permutations. Additionally, we enhance consistency by
introducing an informational prior with cyclic permutations of posteriors,
which turns the regularization term into a similarity measure across
distributions. We demonstrate the effectiveness of our approach on seven
diverse datasets with varying missing ratios, achieving superior performance in
multi-view clustering and generation tasks.
|
[
{
"version": "v1",
"created": "Sun, 16 Feb 2025 08:36:43 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 06:04:20 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Gao",
"Xin",
""
],
[
"Pu",
"Jian",
""
]
] |
TITLE: Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs
ABSTRACT: Multi-View Representation Learning (MVRL) aims to derive a unified
representation from multi-view data by leveraging shared and complementary
information across views. However, when views are irregularly missing, the
incomplete data can lead to representations that lack sufficiency and
consistency. To address this, we propose Multi-View Permutation of Variational
Auto-Encoders (MVP), which excavates invariant relationships between views in
incomplete data. MVP establishes inter-view correspondences in the latent space
of Variational Auto-Encoders, enabling the inference of missing views and the
aggregation of more sufficient information. To derive a valid Evidence Lower
Bound (ELBO) for learning, we apply permutations to randomly reorder variables
for cross-view generation and then partition them by views to maintain
invariant meanings under permutations. Additionally, we enhance consistency by
introducing an informational prior with cyclic permutations of posteriors,
which turns the regularization term into a similarity measure across
distributions. We demonstrate the effectiveness of our approach on seven
diverse datasets with varying missing ratios, achieving superior performance in
multi-view clustering and generation tasks.
|
no_new_dataset
| 0.946695
|
2502.11742
|
Jianyi Peng
|
Jianyi Peng, Fan Lu, Bin Li, Yuan Huang, Sanqing Qu, Guang Chen
|
Range and Bird's Eye View Fused Cross-Modal Visual Place Recognition
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Image-to-point cloud cross-modal Visual Place Recognition (VPR) is a
challenging task where the query is an RGB image, and the database samples are
LiDAR point clouds. Compared to single-modal VPR, this approach benefits from
the widespread availability of RGB cameras and the robustness of point clouds
in providing accurate spatial geometry and distance information. However,
current methods rely on intermediate modalities that capture either the
vertical or horizontal field of view, limiting their ability to fully exploit
the complementary information from both sensors. In this work, we propose an
innovative initial retrieval + re-rank method that effectively combines
information from range (or RGB) images and Bird's Eye View (BEV) images. Our
approach relies solely on a computationally efficient global descriptor
similarity search process to achieve re-ranking. Additionally, we introduce a
novel similarity label supervision technique to maximize the utility of limited
training data. Specifically, we employ points average distance to approximate
appearance similarity and incorporate an adaptive margin, based on similarity
differences, into the vanilla triplet loss. Experimental results on the KITTI
dataset demonstrate that our method significantly outperforms state-of-the-art
approaches.
|
[
{
"version": "v1",
"created": "Mon, 17 Feb 2025 12:29:26 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 10:10:21 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Peng",
"Jianyi",
""
],
[
"Lu",
"Fan",
""
],
[
"Li",
"Bin",
""
],
[
"Huang",
"Yuan",
""
],
[
"Qu",
"Sanqing",
""
],
[
"Chen",
"Guang",
""
]
] |
TITLE: Range and Bird's Eye View Fused Cross-Modal Visual Place Recognition
ABSTRACT: Image-to-point cloud cross-modal Visual Place Recognition (VPR) is a
challenging task where the query is an RGB image, and the database samples are
LiDAR point clouds. Compared to single-modal VPR, this approach benefits from
the widespread availability of RGB cameras and the robustness of point clouds
in providing accurate spatial geometry and distance information. However,
current methods rely on intermediate modalities that capture either the
vertical or horizontal field of view, limiting their ability to fully exploit
the complementary information from both sensors. In this work, we propose an
innovative initial retrieval + re-rank method that effectively combines
information from range (or RGB) images and Bird's Eye View (BEV) images. Our
approach relies solely on a computationally efficient global descriptor
similarity search process to achieve re-ranking. Additionally, we introduce a
novel similarity label supervision technique to maximize the utility of limited
training data. Specifically, we employ points average distance to approximate
appearance similarity and incorporate an adaptive margin, based on similarity
differences, into the vanilla triplet loss. Experimental results on the KITTI
dataset demonstrate that our method significantly outperforms state-of-the-art
approaches.
|
no_new_dataset
| 0.949529
|
2502.15835
|
Zhuchen Cao
|
Zhuchen Cao, Sven Apel, Adish Singla, Vera Demberg
|
Pragmatic Reasoning improves LLM Code Generation
| null | null | null | null |
cs.CL cs.AI cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Large Language Models (LLMs) have demonstrated impressive potential in
translating natural language (NL) instructions into program code. However, user
instructions often contain inherent ambiguities, making it challenging for LLMs
to generate code that accurately reflects the user's true intent. To address
this challenge, researchers have proposed to produce multiple candidates of the
program code and then rerank them to identify the best solution. In this paper,
we propose CodeRSA, a novel code candidate reranking mechanism built upon the
Rational Speech Act (RSA) framework, designed to guide LLMs toward more
comprehensive pragmatic reasoning about user intent. We evaluate CodeRSA using
one of the latest LLMs on a popular code generation dataset. Our experiment
results show that CodeRSA consistently outperforms common baselines, surpasses
the state-of-the-art approach in most cases, and demonstrates robust overall
performance. These findings underscore the effectiveness of integrating
pragmatic reasoning into code candidate reranking, offering a promising
direction for enhancing code generation quality in LLMs.
|
[
{
"version": "v1",
"created": "Thu, 20 Feb 2025 12:44:26 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 13:40:42 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Cao",
"Zhuchen",
""
],
[
"Apel",
"Sven",
""
],
[
"Singla",
"Adish",
""
],
[
"Demberg",
"Vera",
""
]
] |
TITLE: Pragmatic Reasoning improves LLM Code Generation
ABSTRACT: Large Language Models (LLMs) have demonstrated impressive potential in
translating natural language (NL) instructions into program code. However, user
instructions often contain inherent ambiguities, making it challenging for LLMs
to generate code that accurately reflects the user's true intent. To address
this challenge, researchers have proposed to produce multiple candidates of the
program code and then rerank them to identify the best solution. In this paper,
we propose CodeRSA, a novel code candidate reranking mechanism built upon the
Rational Speech Act (RSA) framework, designed to guide LLMs toward more
comprehensive pragmatic reasoning about user intent. We evaluate CodeRSA using
one of the latest LLMs on a popular code generation dataset. Our experiment
results show that CodeRSA consistently outperforms common baselines, surpasses
the state-of-the-art approach in most cases, and demonstrates robust overall
performance. These findings underscore the effectiveness of integrating
pragmatic reasoning into code candidate reranking, offering a promising
direction for enhancing code generation quality in LLMs.
|
no_new_dataset
| 0.95018
|
2502.16622
|
Luis Lara
|
Luis Lara, Lucia Eve Berger, Rajesh Raju
|
Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN
Architectures
|
Upon reflection, the final version of this work does not meet the
author's personal standards for thoroughness and clarity. As a result, the
authors have chosen to withdraw the paper to prevent the dissemination of
work that may not fully reflect the level of quality they strive to maintain
| null | null | null |
eess.IV cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The COVID-19 pandemic strained healthcare resources and prompted discussion
about how machine learning can alleviate physician burdens and contribute to
diagnosis. Chest x-rays (CXRs) are used for diagnosis of COVID-19, but few
studies predict the severity of a patient's condition from CXRs. In this study,
we produce a large COVID severity dataset by merging three sources and
investigate the efficacy of transfer learning using ImageNet- and
CXR-pretrained models and vision transformers (ViTs) in both severity
regression and classification tasks. A pretrained DenseNet161 model performed
the best on the three class severity prediction problem, reaching 80% accuracy
overall and 77.3%, 83.9%, and 70% on mild, moderate and severe cases,
respectively. The ViT had the best regression results, with a mean absolute
error of 0.5676 compared to radiologist-predicted severity scores. The
project's source code is publicly available.
|
[
{
"version": "v1",
"created": "Sun, 23 Feb 2025 15:50:42 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Feb 2025 13:20:09 GMT"
},
{
"version": "v3",
"created": "Fri, 28 Feb 2025 14:34:45 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Lara",
"Luis",
""
],
[
"Berger",
"Lucia Eve",
""
],
[
"Raju",
"Rajesh",
""
]
] |
TITLE: Diagnosing COVID-19 Severity from Chest X-Ray Images Using ViT and CNN
Architectures
ABSTRACT: The COVID-19 pandemic strained healthcare resources and prompted discussion
about how machine learning can alleviate physician burdens and contribute to
diagnosis. Chest x-rays (CXRs) are used for diagnosis of COVID-19, but few
studies predict the severity of a patient's condition from CXRs. In this study,
we produce a large COVID severity dataset by merging three sources and
investigate the efficacy of transfer learning using ImageNet- and
CXR-pretrained models and vision transformers (ViTs) in both severity
regression and classification tasks. A pretrained DenseNet161 model performed
the best on the three class severity prediction problem, reaching 80% accuracy
overall and 77.3%, 83.9%, and 70% on mild, moderate and severe cases,
respectively. The ViT had the best regression results, with a mean absolute
error of 0.5676 compared to radiologist-predicted severity scores. The
project's source code is publicly available.
|
new_dataset
| 0.910863
|
2502.16680
|
Li Rui
|
Rui Li, Xiaowei Zhao
|
AeroReformer: Aerial Referring Transformer for UAV-based Referring Image
Segmentation
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As a novel and challenging task, referring segmentation combines computer
vision and natural language processing to localize and segment objects based on
textual descriptions. While referring image segmentation (RIS) has been
extensively studied in natural images, little attention has been given to
aerial imagery, particularly from unmanned aerial vehicles (UAVs). The unique
challenges of UAV imagery, including complex spatial scales, occlusions, and
varying object orientations, render existing RIS approaches ineffective. A key
limitation has been the lack of UAV-specific datasets, as manually annotating
pixel-level masks and generating textual descriptions is labour-intensive and
time-consuming. To address this gap, we design an automatic labelling pipeline
that leverages pre-existing UAV segmentation datasets and Multimodal Large
Language Models (MLLM) for generating textual descriptions. Furthermore, we
propose Aerial Referring Transformer (AeroReformer), a novel framework for UAV
referring image segmentation (UAV-RIS), featuring a Vision-Language
Cross-Attention Module (VLCAM) for effective cross-modal understanding and a
Rotation-Aware Multi-Scale Fusion (RAMSF) decoder to enhance segmentation
accuracy in aerial scenes. Extensive experiments on two newly developed
datasets demonstrate the superiority of AeroReformer over existing methods,
establishing a new benchmark for UAV-RIS. The datasets and code will be
publicly available at: https://github.com/lironui/AeroReformer.
|
[
{
"version": "v1",
"created": "Sun, 23 Feb 2025 18:49:00 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 17:19:00 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Li",
"Rui",
""
],
[
"Zhao",
"Xiaowei",
""
]
] |
TITLE: AeroReformer: Aerial Referring Transformer for UAV-based Referring Image
Segmentation
ABSTRACT: As a novel and challenging task, referring segmentation combines computer
vision and natural language processing to localize and segment objects based on
textual descriptions. While referring image segmentation (RIS) has been
extensively studied in natural images, little attention has been given to
aerial imagery, particularly from unmanned aerial vehicles (UAVs). The unique
challenges of UAV imagery, including complex spatial scales, occlusions, and
varying object orientations, render existing RIS approaches ineffective. A key
limitation has been the lack of UAV-specific datasets, as manually annotating
pixel-level masks and generating textual descriptions is labour-intensive and
time-consuming. To address this gap, we design an automatic labelling pipeline
that leverages pre-existing UAV segmentation datasets and Multimodal Large
Language Models (MLLM) for generating textual descriptions. Furthermore, we
propose Aerial Referring Transformer (AeroReformer), a novel framework for UAV
referring image segmentation (UAV-RIS), featuring a Vision-Language
Cross-Attention Module (VLCAM) for effective cross-modal understanding and a
Rotation-Aware Multi-Scale Fusion (RAMSF) decoder to enhance segmentation
accuracy in aerial scenes. Extensive experiments on two newly developed
datasets demonstrate the superiority of AeroReformer over existing methods,
establishing a new benchmark for UAV-RIS. The datasets and code will be
publicly available at: https://github.com/lironui/AeroReformer.
|
new_dataset
| 0.963643
|
2502.17009
|
Enea Monzio Compagnoni Mr.
|
Enea Monzio Compagnoni, Rustem Islamov, Frank Norbert Proske, Aurelien
Lucchi
|
Unbiased and Sign Compression in Distributed Learning: Comparing Noise
Resilience via SDEs
|
Accepted at AISTATS 2025 (Oral). arXiv admin note: substantial text
overlap with arXiv:2411.15958
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Distributed methods are essential for handling machine learning pipelines
comprising large-scale models and datasets. However, their benefits often come
at the cost of increased communication overhead between the central server and
agents, which can become the main bottleneck, making training costly or even
unfeasible in such systems. Compression methods such as quantization and
sparsification can alleviate this issue. Still, their robustness to large and
heavy-tailed gradient noise, a phenomenon sometimes observed in language
modeling, remains poorly understood. This work addresses this gap by analyzing
Distributed Compressed SGD (DCSGD) and Distributed SignSGD (DSignSGD) using
stochastic differential equations (SDEs). Our results show that DCSGD with
unbiased compression is more vulnerable to noise in stochastic gradients, while
DSignSGD remains robust, even under large and heavy-tailed noise. Additionally,
we propose new scaling rules for hyperparameter tuning to mitigate performance
degradation due to compression. These findings are empirically validated across
multiple deep learning architectures and datasets, providing practical
recommendations for distributed optimization.
|
[
{
"version": "v1",
"created": "Mon, 24 Feb 2025 09:39:17 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 00:12:11 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Compagnoni",
"Enea Monzio",
""
],
[
"Islamov",
"Rustem",
""
],
[
"Proske",
"Frank Norbert",
""
],
[
"Lucchi",
"Aurelien",
""
]
] |
TITLE: Unbiased and Sign Compression in Distributed Learning: Comparing Noise
Resilience via SDEs
ABSTRACT: Distributed methods are essential for handling machine learning pipelines
comprising large-scale models and datasets. However, their benefits often come
at the cost of increased communication overhead between the central server and
agents, which can become the main bottleneck, making training costly or even
unfeasible in such systems. Compression methods such as quantization and
sparsification can alleviate this issue. Still, their robustness to large and
heavy-tailed gradient noise, a phenomenon sometimes observed in language
modeling, remains poorly understood. This work addresses this gap by analyzing
Distributed Compressed SGD (DCSGD) and Distributed SignSGD (DSignSGD) using
stochastic differential equations (SDEs). Our results show that DCSGD with
unbiased compression is more vulnerable to noise in stochastic gradients, while
DSignSGD remains robust, even under large and heavy-tailed noise. Additionally,
we propose new scaling rules for hyperparameter tuning to mitigate performance
degradation due to compression. These findings are empirically validated across
multiple deep learning architectures and datasets, providing practical
recommendations for distributed optimization.
|
no_new_dataset
| 0.944944
|
2502.17184
|
Yuming Yang
|
Yuming Yang, Yang Nan, Junjie Ye, Shihan Dou, Xiao Wang, Shuo Li,
Huijie Lv, Mingqi Wu, Tao Gui, Qi Zhang, Xuanjing Huang
|
Measuring Data Diversity for Instruction Tuning: A Systematic Analysis
and A Reliable Metric
|
16 pages. The related codes and resources will be released later.
Project page: https://github.com/UmeanNever/NovelSum
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Data diversity is crucial for the instruction tuning of large language
models. Existing studies have explored various diversity-aware data selection
methods to construct high-quality datasets and enhance model performance.
However, the fundamental problem of precisely defining and measuring data
diversity remains underexplored, limiting clear guidance for data engineering.
To address this, we systematically analyze 11 existing diversity measurement
methods by evaluating their correlation with model performance through
extensive fine-tuning experiments. Our results indicate that a reliable
diversity measure should properly account for both inter-sample differences and
the information distribution in the sample space. Building on this, we propose
NovelSum, a new diversity metric based on sample-level "novelty." Experiments
on both simulated and real-world data show that NovelSum accurately captures
diversity variations and achieves a 0.97 correlation with instruction-tuned
model performance, highlighting its value in guiding data engineering
practices. With NovelSum as an optimization objective, we further develop a
greedy, diversity-oriented data selection strategy that outperforms existing
approaches, validating both the effectiveness and practical significance of our
metric.
|
[
{
"version": "v1",
"created": "Mon, 24 Feb 2025 14:20:22 GMT"
},
{
"version": "v2",
"created": "Tue, 25 Feb 2025 06:56:39 GMT"
},
{
"version": "v3",
"created": "Thu, 27 Feb 2025 12:59:58 GMT"
},
{
"version": "v4",
"created": "Fri, 28 Feb 2025 08:44:08 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Yang",
"Yuming",
""
],
[
"Nan",
"Yang",
""
],
[
"Ye",
"Junjie",
""
],
[
"Dou",
"Shihan",
""
],
[
"Wang",
"Xiao",
""
],
[
"Li",
"Shuo",
""
],
[
"Lv",
"Huijie",
""
],
[
"Wu",
"Mingqi",
""
],
[
"Gui",
"Tao",
""
],
[
"Zhang",
"Qi",
""
],
[
"Huang",
"Xuanjing",
""
]
] |
TITLE: Measuring Data Diversity for Instruction Tuning: A Systematic Analysis
and A Reliable Metric
ABSTRACT: Data diversity is crucial for the instruction tuning of large language
models. Existing studies have explored various diversity-aware data selection
methods to construct high-quality datasets and enhance model performance.
However, the fundamental problem of precisely defining and measuring data
diversity remains underexplored, limiting clear guidance for data engineering.
To address this, we systematically analyze 11 existing diversity measurement
methods by evaluating their correlation with model performance through
extensive fine-tuning experiments. Our results indicate that a reliable
diversity measure should properly account for both inter-sample differences and
the information distribution in the sample space. Building on this, we propose
NovelSum, a new diversity metric based on sample-level "novelty." Experiments
on both simulated and real-world data show that NovelSum accurately captures
diversity variations and achieves a 0.97 correlation with instruction-tuned
model performance, highlighting its value in guiding data engineering
practices. With NovelSum as an optimization objective, we further develop a
greedy, diversity-oriented data selection strategy that outperforms existing
approaches, validating both the effectiveness and practical significance of our
metric.
|
no_new_dataset
| 0.94428
|
2502.17481
|
Cheol-Hui Lee
|
Cheol-Hui Lee, Hakseung Kim, Byung C. Yoon, Dong-Joo Kim
|
Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid
Self-Supervised Learning Framework
|
18 pages, 5 figures
| null | null | null |
eess.SP cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sleep is essential for maintaining human health and quality of life.
Analyzing physiological signals during sleep is critical in assessing sleep
quality and diagnosing sleep disorders. However, manual diagnoses by clinicians
are time-intensive and subjective. Despite advances in deep learning that have
enhanced automation, these approaches remain heavily dependent on large-scale
labeled datasets. This study introduces SynthSleepNet, a multimodal hybrid
self-supervised learning framework designed for analyzing polysomnography (PSG)
data. SynthSleepNet effectively integrates masked prediction and contrastive
learning to leverage complementary features across multiple modalities,
including electroencephalogram (EEG), electrooculography (EOG),
electromyography (EMG), and electrocardiogram (ECG). This approach enables the
model to learn highly expressive representations of PSG data. Furthermore, a
temporal context module based on Mamba was developed to efficiently capture
contextual information across signals. SynthSleepNet achieved superior
performance compared to state-of-the-art methods across three downstream tasks:
sleep-stage classification, apnea detection, and hypopnea detection, with
accuracies of 89.89%, 99.75%, and 89.60%, respectively. The model demonstrated
robust performance in a semi-supervised learning environment with limited
labels, achieving accuracies of 87.98%, 99.37%, and 77.52% in the same tasks.
These results underscore the potential of the model as a foundational tool for
the comprehensive analysis of PSG data. SynthSleepNet demonstrates
comprehensively superior performance across multiple downstream tasks compared
to other methodologies, making it expected to set a new standard for sleep
disorder monitoring and diagnostic systems.
|
[
{
"version": "v1",
"created": "Tue, 18 Feb 2025 10:11:50 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 18:56:25 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Lee",
"Cheol-Hui",
""
],
[
"Kim",
"Hakseung",
""
],
[
"Yoon",
"Byung C.",
""
],
[
"Kim",
"Dong-Joo",
""
]
] |
TITLE: Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid
Self-Supervised Learning Framework
ABSTRACT: Sleep is essential for maintaining human health and quality of life.
Analyzing physiological signals during sleep is critical in assessing sleep
quality and diagnosing sleep disorders. However, manual diagnoses by clinicians
are time-intensive and subjective. Despite advances in deep learning that have
enhanced automation, these approaches remain heavily dependent on large-scale
labeled datasets. This study introduces SynthSleepNet, a multimodal hybrid
self-supervised learning framework designed for analyzing polysomnography (PSG)
data. SynthSleepNet effectively integrates masked prediction and contrastive
learning to leverage complementary features across multiple modalities,
including electroencephalogram (EEG), electrooculography (EOG),
electromyography (EMG), and electrocardiogram (ECG). This approach enables the
model to learn highly expressive representations of PSG data. Furthermore, a
temporal context module based on Mamba was developed to efficiently capture
contextual information across signals. SynthSleepNet achieved superior
performance compared to state-of-the-art methods across three downstream tasks:
sleep-stage classification, apnea detection, and hypopnea detection, with
accuracies of 89.89%, 99.75%, and 89.60%, respectively. The model demonstrated
robust performance in a semi-supervised learning environment with limited
labels, achieving accuracies of 87.98%, 99.37%, and 77.52% in the same tasks.
These results underscore the potential of the model as a foundational tool for
the comprehensive analysis of PSG data. SynthSleepNet demonstrates
comprehensively superior performance across multiple downstream tasks compared
to other methodologies, making it expected to set a new standard for sleep
disorder monitoring and diagnostic systems.
|
no_new_dataset
| 0.948775
|
2502.17690
|
Zhixin Lu
|
Zhixin Lu, {\L}ukasz Ku\'smierz, Stefan Mihalas
|
A Fokker-Planck-Based Loss Function that Bridges Dynamics with Density
Estimation
|
Under review by the ICML
| null | null | null |
nlin.CD cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We have derived a novel loss function from the Fokker-Planck equation that
links dynamical system models with their probability density functions,
demonstrating its utility in model identification and density estimation. In
the first application, we show that this loss function can enable the
extraction of dynamical parameters from non-temporal datasets, including
timestamp-free measurements from steady non-equilibrium systems such as noisy
Lorenz systems and gene regulatory networks. In the second application, when
coupled with a density estimator, this loss facilitates density estimation when
the dynamic equations are known. For density estimation, we propose a density
estimator that integrates a Gaussian Mixture Model with a normalizing flow
model. It simultaneously estimates normalized density, energy, and score
functions from both empirical data and dynamics. It is compatible with a
variety of data-based training methodologies, including maximum likelihood and
score matching. It features a latent space akin to a modern Hopfield network,
where the inherent Hopfield energy effectively assigns low densities to
sparsely populated data regions, addressing common challenges in neural density
estimators. Additionally, this Hopfield-like energy enables direct and rapid
data manipulation through the Concave-Convex Procedure (CCCP) rule,
facilitating tasks such as denoising and clustering. Our work demonstrates a
principled framework for leveraging the complex interdependencies between
dynamics and density estimation, as illustrated through synthetic examples that
clarify the underlying theoretical intuitions.
|
[
{
"version": "v1",
"created": "Mon, 24 Feb 2025 22:27:25 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Feb 2025 22:11:09 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Lu",
"Zhixin",
""
],
[
"Kuśmierz",
"Łukasz",
""
],
[
"Mihalas",
"Stefan",
""
]
] |
TITLE: A Fokker-Planck-Based Loss Function that Bridges Dynamics with Density
Estimation
ABSTRACT: We have derived a novel loss function from the Fokker-Planck equation that
links dynamical system models with their probability density functions,
demonstrating its utility in model identification and density estimation. In
the first application, we show that this loss function can enable the
extraction of dynamical parameters from non-temporal datasets, including
timestamp-free measurements from steady non-equilibrium systems such as noisy
Lorenz systems and gene regulatory networks. In the second application, when
coupled with a density estimator, this loss facilitates density estimation when
the dynamic equations are known. For density estimation, we propose a density
estimator that integrates a Gaussian Mixture Model with a normalizing flow
model. It simultaneously estimates normalized density, energy, and score
functions from both empirical data and dynamics. It is compatible with a
variety of data-based training methodologies, including maximum likelihood and
score matching. It features a latent space akin to a modern Hopfield network,
where the inherent Hopfield energy effectively assigns low densities to
sparsely populated data regions, addressing common challenges in neural density
estimators. Additionally, this Hopfield-like energy enables direct and rapid
data manipulation through the Concave-Convex Procedure (CCCP) rule,
facilitating tasks such as denoising and clustering. Our work demonstrates a
principled framework for leveraging the complex interdependencies between
dynamics and density estimation, as illustrated through synthetic examples that
clarify the underlying theoretical intuitions.
|
no_new_dataset
| 0.947962
|
2502.17749
|
Shinwoo Park
|
Shinwoo Park, Hyundong Jin, Jeong-won Cha, Yo-Sub Han
|
Detection of LLM-Paraphrased Code and Identification of the Responsible
LLM Using Coding Style Features
| null | null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Recent progress in large language models (LLMs) for code generation has
raised serious concerns about intellectual property protection. Malicious users
can exploit LLMs to produce paraphrased versions of proprietary code that
closely resemble the original. While the potential for LLM-assisted code
paraphrasing continues to grow, research on detecting it remains limited,
underscoring an urgent need for detection system. We respond to this need by
proposing two tasks. The first task is to detect whether code generated by an
LLM is a paraphrased version of original human-written code. The second task is
to identify which LLM is used to paraphrase the original code. For these tasks,
we construct a dataset LPcode consisting of pairs of human-written code and
LLM-paraphrased code using various LLMs.
We statistically confirm significant differences in the coding styles of
human-written and LLM-paraphrased code, particularly in terms of naming
consistency, code structure, and readability. Based on these findings, we
develop LPcodedec, a detection method that identifies paraphrase relationships
between human-written and LLM-generated code, and discover which LLM is used
for the paraphrasing. LPcodedec outperforms the best baselines in two tasks,
improving F1 scores by 2.64% and 15.17% while achieving speedups of 1,343x and
213x, respectively. Our code and data are available at
https://github.com/Shinwoo-Park/detecting_llm_paraphrased_code_via_coding_style_features.
|
[
{
"version": "v1",
"created": "Tue, 25 Feb 2025 00:58:06 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 08:06:00 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Park",
"Shinwoo",
""
],
[
"Jin",
"Hyundong",
""
],
[
"Cha",
"Jeong-won",
""
],
[
"Han",
"Yo-Sub",
""
]
] |
TITLE: Detection of LLM-Paraphrased Code and Identification of the Responsible
LLM Using Coding Style Features
ABSTRACT: Recent progress in large language models (LLMs) for code generation has
raised serious concerns about intellectual property protection. Malicious users
can exploit LLMs to produce paraphrased versions of proprietary code that
closely resemble the original. While the potential for LLM-assisted code
paraphrasing continues to grow, research on detecting it remains limited,
underscoring an urgent need for detection system. We respond to this need by
proposing two tasks. The first task is to detect whether code generated by an
LLM is a paraphrased version of original human-written code. The second task is
to identify which LLM is used to paraphrase the original code. For these tasks,
we construct a dataset LPcode consisting of pairs of human-written code and
LLM-paraphrased code using various LLMs.
We statistically confirm significant differences in the coding styles of
human-written and LLM-paraphrased code, particularly in terms of naming
consistency, code structure, and readability. Based on these findings, we
develop LPcodedec, a detection method that identifies paraphrase relationships
between human-written and LLM-generated code, and discover which LLM is used
for the paraphrasing. LPcodedec outperforms the best baselines in two tasks,
improving F1 scores by 2.64% and 15.17% while achieving speedups of 1,343x and
213x, respectively. Our code and data are available at
https://github.com/Shinwoo-Park/detecting_llm_paraphrased_code_via_coding_style_features.
|
new_dataset
| 0.968411
|
2502.18842
|
Muhammad Angga Muttaqien
|
Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai, Domae Yukiyasu
|
Attention-Guided Integration of CLIP and SAM for Precise Object Masking
in Robotic Manipulation
| null | null | null | null |
cs.RO cs.AI cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
This paper introduces a novel pipeline to enhance the precision of object
masking for robotic manipulation within the specific domain of masking products
in convenience stores. The approach integrates two advanced AI models, CLIP and
SAM, focusing on their synergistic combination and the effective use of
multimodal data (image and text). Emphasis is placed on utilizing
gradient-based attention mechanisms and customized datasets to fine-tune
performance. While CLIP, SAM, and Grad- CAM are established components, their
integration within this structured pipeline represents a significant
contribution to the field. The resulting segmented masks, generated through
this combined approach, can be effectively utilized as inputs for robotic
systems, enabling more precise and adaptive object manipulation in the context
of convenience store products.
|
[
{
"version": "v1",
"created": "Wed, 26 Feb 2025 05:30:46 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 02:20:15 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Muttaqien",
"Muhammad A.",
""
],
[
"Motoda",
"Tomohiro",
""
],
[
"Hanai",
"Ryo",
""
],
[
"Yukiyasu",
"Domae",
""
]
] |
TITLE: Attention-Guided Integration of CLIP and SAM for Precise Object Masking
in Robotic Manipulation
ABSTRACT: This paper introduces a novel pipeline to enhance the precision of object
masking for robotic manipulation within the specific domain of masking products
in convenience stores. The approach integrates two advanced AI models, CLIP and
SAM, focusing on their synergistic combination and the effective use of
multimodal data (image and text). Emphasis is placed on utilizing
gradient-based attention mechanisms and customized datasets to fine-tune
performance. While CLIP, SAM, and Grad- CAM are established components, their
integration within this structured pipeline represents a significant
contribution to the field. The resulting segmented masks, generated through
this combined approach, can be effectively utilized as inputs for robotic
systems, enabling more precise and adaptive object manipulation in the context
of convenience store products.
|
no_new_dataset
| 0.950915
|
2502.18860
|
Md Mehrab Tanjim
|
Md Mehrab Tanjim, Ryan A. Rossi, Mike Rimer, Xiang Chen, Sungchul Kim,
Vaishnavi Muppala, Tong Yu, Zhengmian Hu, Ritwik Sinha, Wei Zhang, Iftikhar
Ahamath Burhanuddin, Franck Dernoncourt
|
Exploring Rewriting Approaches for Different Conversational Tasks
|
Preprint
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Conversational assistants often require a question rewriting algorithm that
leverages a subset of past interactions to provide a more meaningful (accurate)
answer to the user's question or request. However, the exact rewriting approach
may often depend on the use case and application-specific tasks supported by
the conversational assistant, among other constraints. In this paper, we
systematically investigate two different approaches, denoted as rewriting and
fusion, on two fundamentally different generation tasks, including a
text-to-text generation task and a multimodal generative task that takes as
input text and generates a visualization or data table that answers the user's
question. Our results indicate that the specific rewriting or fusion approach
highly depends on the underlying use case and generative task. In particular,
we find that for a conversational question-answering assistant, the query
rewriting approach performs best, whereas for a data analysis assistant that
generates visualizations and data tables based on the user's conversation with
the assistant, the fusion approach works best. Notably, we explore two datasets
for the data analysis assistant use case, for short and long conversations, and
we find that query fusion always performs better, whereas for the
conversational text-based question-answering, the query rewrite approach
performs best.
|
[
{
"version": "v1",
"created": "Wed, 26 Feb 2025 06:05:29 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 04:18:19 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Tanjim",
"Md Mehrab",
""
],
[
"Rossi",
"Ryan A.",
""
],
[
"Rimer",
"Mike",
""
],
[
"Chen",
"Xiang",
""
],
[
"Kim",
"Sungchul",
""
],
[
"Muppala",
"Vaishnavi",
""
],
[
"Yu",
"Tong",
""
],
[
"Hu",
"Zhengmian",
""
],
[
"Sinha",
"Ritwik",
""
],
[
"Zhang",
"Wei",
""
],
[
"Burhanuddin",
"Iftikhar Ahamath",
""
],
[
"Dernoncourt",
"Franck",
""
]
] |
TITLE: Exploring Rewriting Approaches for Different Conversational Tasks
ABSTRACT: Conversational assistants often require a question rewriting algorithm that
leverages a subset of past interactions to provide a more meaningful (accurate)
answer to the user's question or request. However, the exact rewriting approach
may often depend on the use case and application-specific tasks supported by
the conversational assistant, among other constraints. In this paper, we
systematically investigate two different approaches, denoted as rewriting and
fusion, on two fundamentally different generation tasks, including a
text-to-text generation task and a multimodal generative task that takes as
input text and generates a visualization or data table that answers the user's
question. Our results indicate that the specific rewriting or fusion approach
highly depends on the underlying use case and generative task. In particular,
we find that for a conversational question-answering assistant, the query
rewriting approach performs best, whereas for a data analysis assistant that
generates visualizations and data tables based on the user's conversation with
the assistant, the fusion approach works best. Notably, we explore two datasets
for the data analysis assistant use case, for short and long conversations, and
we find that query fusion always performs better, whereas for the
conversational text-based question-answering, the query rewrite approach
performs best.
|
no_new_dataset
| 0.949153
|
2502.19104
|
Michelle Kappl
|
Michelle Kappl
|
Are All Spanish Doctors Male? Evaluating Gender Bias in German Machine
Translation
|
ISCA/ITG Workshop on Diversity in Large Speech and Language Models
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present WinoMTDE, a new gender bias evaluation test set designed to assess
occupational stereotyping and underrepresentation in German machine translation
(MT) systems. Building on the automatic evaluation method introduced by
arXiv:1906.00591v1, we extend the approach to German, a language with
grammatical gender. The WinoMTDE dataset comprises 288 German sentences that
are balanced in regard to gender, as well as stereotype, which was annotated
using German labor statistics. We conduct a large-scale evaluation of five
widely used MT systems and a large language model. Our results reveal
persistent bias in most models, with the LLM outperforming traditional systems.
The dataset and evaluation code are publicly available under
https://github.com/michellekappl/mt_gender_german.
|
[
{
"version": "v1",
"created": "Wed, 26 Feb 2025 12:46:59 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 15:00:01 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Kappl",
"Michelle",
""
]
] |
TITLE: Are All Spanish Doctors Male? Evaluating Gender Bias in German Machine
Translation
ABSTRACT: We present WinoMTDE, a new gender bias evaluation test set designed to assess
occupational stereotyping and underrepresentation in German machine translation
(MT) systems. Building on the automatic evaluation method introduced by
arXiv:1906.00591v1, we extend the approach to German, a language with
grammatical gender. The WinoMTDE dataset comprises 288 German sentences that
are balanced in regard to gender, as well as stereotype, which was annotated
using German labor statistics. We conduct a large-scale evaluation of five
widely used MT systems and a large language model. Our results reveal
persistent bias in most models, with the LLM outperforming traditional systems.
The dataset and evaluation code are publicly available under
https://github.com/michellekappl/mt_gender_german.
|
new_dataset
| 0.955402
|
2502.19635
|
Youran Zhou
|
Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal
|
Developing robust methods to handle missing data in real-world
applications effectively
|
This work was presented at the ECML PKDD 2024 PhD Forum.
https://ecmlpkdd. org/2024/program-accepted-phd-forum/
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Missing data is a pervasive challenge spanning diverse data types, including
tabular, sensor data, time-series, images and so on. Its origins are
multifaceted, resulting in various missing mechanisms. Prior research in this
field has predominantly revolved around the assumption of the Missing
Completely At Random (MCAR) mechanism. However, Missing At Random (MAR) and
Missing Not At Random (MNAR) mechanisms, though equally prevalent, have often
remained underexplored despite their significant influence. This PhD project
presents a comprehensive research agenda designed to investigate the
implications of diverse missing data mechanisms. The principal aim is to devise
robust methodologies capable of effectively handling missing data while
accommodating the unique characteristics of MCAR, MAR, and MNAR mechanisms. By
addressing these gaps, this research contributes to an enriched understanding
of the challenges posed by missing data across various industries and data
modalities. It seeks to provide practical solutions that enable the effective
management of missing data, empowering researchers and practitioners to
leverage incomplete datasets confidently.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 00:00:28 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 11:26:39 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Zhou",
"Youran",
""
],
[
"Bouadjenek",
"Mohamed Reda",
""
],
[
"Aryal",
"Sunil",
""
]
] |
TITLE: Developing robust methods to handle missing data in real-world
applications effectively
ABSTRACT: Missing data is a pervasive challenge spanning diverse data types, including
tabular, sensor data, time-series, images and so on. Its origins are
multifaceted, resulting in various missing mechanisms. Prior research in this
field has predominantly revolved around the assumption of the Missing
Completely At Random (MCAR) mechanism. However, Missing At Random (MAR) and
Missing Not At Random (MNAR) mechanisms, though equally prevalent, have often
remained underexplored despite their significant influence. This PhD project
presents a comprehensive research agenda designed to investigate the
implications of diverse missing data mechanisms. The principal aim is to devise
robust methodologies capable of effectively handling missing data while
accommodating the unique characteristics of MCAR, MAR, and MNAR mechanisms. By
addressing these gaps, this research contributes to an enriched understanding
of the challenges posed by missing data across various industries and data
modalities. It seeks to provide practical solutions that enable the effective
management of missing data, empowering researchers and practitioners to
leverage incomplete datasets confidently.
|
no_new_dataset
| 0.943295
|
2502.19751
|
Zeqi Ma
|
Jiaxing Li and Lin Jiang and Zeqi Ma and Kaihang Jiang and Xiaozhao
Fang and Jie Wen
|
Lightweight Contrastive Distilled Hashing for Online Cross-modal
Retrieval
|
Accepted by AAAI 2025
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Deep online cross-modal hashing has gained much attention from researchers
recently, as its promising applications with low storage requirement, fast
retrieval efficiency and cross modality adaptive, etc. However, there still
exists some technical hurdles that hinder its applications, e.g., 1) how to
extract the coexistent semantic relevance of cross-modal data, 2) how to
achieve competitive performance when handling the real time data streams, 3)
how to transfer the knowledge learned from offline to online training in a
lightweight manner. To address these problems, this paper proposes a
lightweight contrastive distilled hashing (LCDH) for cross-modal retrieval, by
innovatively bridging the offline and online cross-modal hashing by similarity
matrix approximation in a knowledge distillation framework. Specifically, in
the teacher network, LCDH first extracts the cross-modal features by the
contrastive language-image pre-training (CLIP), which are further fed into an
attention module for representation enhancement after feature fusion. Then, the
output of the attention module is fed into a FC layer to obtain hash codes for
aligning the sizes of similarity matrices for online and offline training. In
the student network, LCDH extracts the visual and textual features by
lightweight models, and then the features are fed into a FC layer to generate
binary codes. Finally, by approximating the similarity matrices, the
performance of online hashing in the lightweight student network can be
enhanced by the supervision of coexistent semantic relevance that is distilled
from the teacher network. Experimental results on three widely used datasets
demonstrate that LCDH outperforms some state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 04:31:17 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 02:33:25 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Li",
"Jiaxing",
""
],
[
"Jiang",
"Lin",
""
],
[
"Ma",
"Zeqi",
""
],
[
"Jiang",
"Kaihang",
""
],
[
"Fang",
"Xiaozhao",
""
],
[
"Wen",
"Jie",
""
]
] |
TITLE: Lightweight Contrastive Distilled Hashing for Online Cross-modal
Retrieval
ABSTRACT: Deep online cross-modal hashing has gained much attention from researchers
recently, as its promising applications with low storage requirement, fast
retrieval efficiency and cross modality adaptive, etc. However, there still
exists some technical hurdles that hinder its applications, e.g., 1) how to
extract the coexistent semantic relevance of cross-modal data, 2) how to
achieve competitive performance when handling the real time data streams, 3)
how to transfer the knowledge learned from offline to online training in a
lightweight manner. To address these problems, this paper proposes a
lightweight contrastive distilled hashing (LCDH) for cross-modal retrieval, by
innovatively bridging the offline and online cross-modal hashing by similarity
matrix approximation in a knowledge distillation framework. Specifically, in
the teacher network, LCDH first extracts the cross-modal features by the
contrastive language-image pre-training (CLIP), which are further fed into an
attention module for representation enhancement after feature fusion. Then, the
output of the attention module is fed into a FC layer to obtain hash codes for
aligning the sizes of similarity matrices for online and offline training. In
the student network, LCDH extracts the visual and textual features by
lightweight models, and then the features are fed into a FC layer to generate
binary codes. Finally, by approximating the similarity matrices, the
performance of online hashing in the lightweight student network can be
enhanced by the supervision of coexistent semantic relevance that is distilled
from the teacher network. Experimental results on three widely used datasets
demonstrate that LCDH outperforms some state-of-the-art methods.
|
no_new_dataset
| 0.949576
|
2502.19989
|
Surajit Ghosh
|
Hugo Retief, Mariangel Garcia Andarcia, Chris Dickens, Surajit Ghosh
|
Dam Volume Prediction Model Development Using ML Algorithms
|
22 pages, 18 Figures and 4 Tables
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Reliable reservoir volume estimates are crucial for water resource
management, especially in arid and semi-arid regions. The present study
investigates applying three machine learning regression techniques - Gradient
Boosting, Random Forest, and ElasticNet to predict key dam performance
characteristics of the Loskop Dam in South Africa. The models were trained and
validated on a dataset comprising geospatial elevation measurements paired with
corresponding reservoir supply capacity values. The best-performing approach
was a threshold-based blended model that combined random forest for higher
volumes with Ridge regression for lower volumes. This model achieved an RMSE of
4.88 MCM and an R2 of 0.99. These findings highlight the ability of ensemble
learning techniques to capture complex relationships in dam datasets and
underscore their practical utility for reliable dam performance modelling in
real-world water resource management scenarios.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 11:14:14 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 04:28:01 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Retief",
"Hugo",
""
],
[
"Andarcia",
"Mariangel Garcia",
""
],
[
"Dickens",
"Chris",
""
],
[
"Ghosh",
"Surajit",
""
]
] |
TITLE: Dam Volume Prediction Model Development Using ML Algorithms
ABSTRACT: Reliable reservoir volume estimates are crucial for water resource
management, especially in arid and semi-arid regions. The present study
investigates applying three machine learning regression techniques - Gradient
Boosting, Random Forest, and ElasticNet to predict key dam performance
characteristics of the Loskop Dam in South Africa. The models were trained and
validated on a dataset comprising geospatial elevation measurements paired with
corresponding reservoir supply capacity values. The best-performing approach
was a threshold-based blended model that combined random forest for higher
volumes with Ridge regression for lower volumes. This model achieved an RMSE of
4.88 MCM and an R2 of 0.99. These findings highlight the ability of ensemble
learning techniques to capture complex relationships in dam datasets and
underscore their practical utility for reliable dam performance modelling in
real-world water resource management scenarios.
|
no_new_dataset
| 0.940681
|
2502.20037
|
Hongyu Deng
|
Hongyu Deng, Tianfan Xue, He Chen
|
FuseGrasp: Radar-Camera Fusion for Robotic Grasping of Transparent
Objects
|
16 pages, 20 figures, accepted by IEEE TMC
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Transparent objects are prevalent in everyday environments, but their
distinct physical properties pose significant challenges for camera-guided
robotic arms. Current research is mainly dependent on camera-only approaches,
which often falter in suboptimal conditions, such as low-light environments. In
response to this challenge, we present FuseGrasp, the first radar-camera fusion
system tailored to enhance the transparent objects manipulation. FuseGrasp
exploits the weak penetrating property of millimeter-wave (mmWave) signals,
which causes transparent materials to appear opaque, and combines it with the
precise motion control of a robotic arm to acquire high-quality mmWave radar
images of transparent objects. The system employs a carefully designed deep
neural network to fuse radar and camera imagery, thereby improving depth
completion and elevating the success rate of object grasping. Nevertheless,
training FuseGrasp effectively is non-trivial, due to limited radar image
datasets for transparent objects. We address this issue utilizing large RGB-D
dataset, and propose an effective two-stage training approach: we first
pre-train FuseGrasp on a large public RGB-D dataset of transparent objects,
then fine-tune it on a self-built small RGB-D-Radar dataset. Furthermore, as a
byproduct, FuseGrasp can determine the composition of transparent objects, such
as glass or plastic, leveraging the material identification capability of
mmWave radar. This identification result facilitates the robotic arm in
modulating its grip force appropriately. Extensive testing reveals that
FuseGrasp significantly improves the accuracy of depth reconstruction and
material identification for transparent objects. Moreover, real-world robotic
trials have confirmed that FuseGrasp markedly enhances the handling of
transparent items. A video demonstration of FuseGrasp is available at
https://youtu.be/MWDqv0sRSok.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 12:27:07 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 08:42:19 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Deng",
"Hongyu",
""
],
[
"Xue",
"Tianfan",
""
],
[
"Chen",
"He",
""
]
] |
TITLE: FuseGrasp: Radar-Camera Fusion for Robotic Grasping of Transparent
Objects
ABSTRACT: Transparent objects are prevalent in everyday environments, but their
distinct physical properties pose significant challenges for camera-guided
robotic arms. Current research is mainly dependent on camera-only approaches,
which often falter in suboptimal conditions, such as low-light environments. In
response to this challenge, we present FuseGrasp, the first radar-camera fusion
system tailored to enhance the transparent objects manipulation. FuseGrasp
exploits the weak penetrating property of millimeter-wave (mmWave) signals,
which causes transparent materials to appear opaque, and combines it with the
precise motion control of a robotic arm to acquire high-quality mmWave radar
images of transparent objects. The system employs a carefully designed deep
neural network to fuse radar and camera imagery, thereby improving depth
completion and elevating the success rate of object grasping. Nevertheless,
training FuseGrasp effectively is non-trivial, due to limited radar image
datasets for transparent objects. We address this issue utilizing large RGB-D
dataset, and propose an effective two-stage training approach: we first
pre-train FuseGrasp on a large public RGB-D dataset of transparent objects,
then fine-tune it on a self-built small RGB-D-Radar dataset. Furthermore, as a
byproduct, FuseGrasp can determine the composition of transparent objects, such
as glass or plastic, leveraging the material identification capability of
mmWave radar. This identification result facilitates the robotic arm in
modulating its grip force appropriately. Extensive testing reveals that
FuseGrasp significantly improves the accuracy of depth reconstruction and
material identification for transparent objects. Moreover, real-world robotic
trials have confirmed that FuseGrasp markedly enhances the handling of
transparent items. A video demonstration of FuseGrasp is available at
https://youtu.be/MWDqv0sRSok.
|
no_new_dataset
| 0.915583
|
2502.20077
|
Zijie Zhou
|
Zijie Zhou, Zhangshuo Qi, Luqi Cheng, Guangming Xiong
|
SegLocNet: Multimodal Localization Network for Autonomous Driving via
Bird's-Eye-View Segmentation
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Robust and accurate localization is critical for autonomous driving.
Traditional GNSS-based localization methods suffer from signal occlusion and
multipath effects in urban environments. Meanwhile, methods relying on
high-definition (HD) maps are constrained by the high costs associated with the
construction and maintenance of HD maps. Standard-definition (SD) maps-based
methods, on the other hand, often exhibit unsatisfactory performance or poor
generalization ability due to overfitting. To address these challenges, we
propose SegLocNet, a multimodal GNSS-free localization network that achieves
precise localization using bird's-eye-view (BEV) semantic segmentation.
SegLocNet employs a BEV segmentation network to generate semantic maps from
multiple sensor inputs, followed by an exhaustive matching process to estimate
the vehicle's ego pose. This approach avoids the limitations of
regression-based pose estimation and maintains high interpretability and
generalization. By introducing a unified map representation, our method can be
applied to both HD and SD maps without any modifications to the network
architecture, thereby balancing localization accuracy and area coverage.
Extensive experiments on the nuScenes and Argoverse datasets demonstrate that
our method outperforms the current state-of-the-art methods, and that our
method can accurately estimate the ego pose in urban environments without
relying on GNSS, while maintaining strong generalization ability. Our code and
pre-trained model will be released publicly.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 13:34:55 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 14:25:18 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Zhou",
"Zijie",
""
],
[
"Qi",
"Zhangshuo",
""
],
[
"Cheng",
"Luqi",
""
],
[
"Xiong",
"Guangming",
""
]
] |
TITLE: SegLocNet: Multimodal Localization Network for Autonomous Driving via
Bird's-Eye-View Segmentation
ABSTRACT: Robust and accurate localization is critical for autonomous driving.
Traditional GNSS-based localization methods suffer from signal occlusion and
multipath effects in urban environments. Meanwhile, methods relying on
high-definition (HD) maps are constrained by the high costs associated with the
construction and maintenance of HD maps. Standard-definition (SD) maps-based
methods, on the other hand, often exhibit unsatisfactory performance or poor
generalization ability due to overfitting. To address these challenges, we
propose SegLocNet, a multimodal GNSS-free localization network that achieves
precise localization using bird's-eye-view (BEV) semantic segmentation.
SegLocNet employs a BEV segmentation network to generate semantic maps from
multiple sensor inputs, followed by an exhaustive matching process to estimate
the vehicle's ego pose. This approach avoids the limitations of
regression-based pose estimation and maintains high interpretability and
generalization. By introducing a unified map representation, our method can be
applied to both HD and SD maps without any modifications to the network
architecture, thereby balancing localization accuracy and area coverage.
Extensive experiments on the nuScenes and Argoverse datasets demonstrate that
our method outperforms the current state-of-the-art methods, and that our
method can accurately estimate the ego pose in urban environments without
relying on GNSS, while maintaining strong generalization ability. Our code and
pre-trained model will be released publicly.
|
no_new_dataset
| 0.94868
|
2502.20104
|
Xuzheng Yang
|
Xuzheng Yang, Junzhuo Liu, Peng Wang, Guoqing Wang, Yang Yang, Heng
Tao Shen
|
New Dataset and Methods for Fine-Grained Compositional Referring
Expression Comprehension via Specialist-MLLM Collaboration
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Referring Expression Comprehension (REC) is a foundational cross-modal task
that evaluates the interplay of language understanding, image comprehension,
and language-to-image grounding. To advance this field, we introduce a new REC
dataset with two key features. First, it is designed with controllable
difficulty levels, requiring fine-grained reasoning across object categories,
attributes, and relationships. Second, it incorporates negative text and images
generated through fine-grained editing, explicitly testing a model's ability to
reject non-existent targets, an often-overlooked yet critical challenge in
existing datasets. To address fine-grained compositional REC, we propose novel
methods based on a Specialist-MLLM collaboration framework, leveraging the
complementary strengths of them: Specialist Models handle simpler tasks
efficiently, while MLLMs are better suited for complex reasoning. Based on this
synergy, we introduce two collaborative strategies. The first, Slow-Fast
Adaptation (SFA), employs a routing mechanism to adaptively delegate simple
tasks to Specialist Models and complex tasks to MLLMs. Additionally, common
error patterns in both models are mitigated through a target-refocus strategy.
The second, Candidate Region Selection (CRS), generates multiple bounding box
candidates based on Specialist Model and uses the advanced reasoning
capabilities of MLLMs to identify the correct target. Extensive experiments on
our dataset and other challenging compositional benchmarks validate the
effectiveness of our approaches. The SFA strategy achieves a trade-off between
localization accuracy and efficiency, and the CRS strategy greatly boosts the
performance of both Specialist Models and MLLMs. We aim for this work to offer
valuable insights into solving complex real-world tasks by strategically
combining existing tools for maximum effectiveness, rather than reinventing
them.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 13:58:44 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 07:36:32 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Yang",
"Xuzheng",
""
],
[
"Liu",
"Junzhuo",
""
],
[
"Wang",
"Peng",
""
],
[
"Wang",
"Guoqing",
""
],
[
"Yang",
"Yang",
""
],
[
"Shen",
"Heng Tao",
""
]
] |
TITLE: New Dataset and Methods for Fine-Grained Compositional Referring
Expression Comprehension via Specialist-MLLM Collaboration
ABSTRACT: Referring Expression Comprehension (REC) is a foundational cross-modal task
that evaluates the interplay of language understanding, image comprehension,
and language-to-image grounding. To advance this field, we introduce a new REC
dataset with two key features. First, it is designed with controllable
difficulty levels, requiring fine-grained reasoning across object categories,
attributes, and relationships. Second, it incorporates negative text and images
generated through fine-grained editing, explicitly testing a model's ability to
reject non-existent targets, an often-overlooked yet critical challenge in
existing datasets. To address fine-grained compositional REC, we propose novel
methods based on a Specialist-MLLM collaboration framework, leveraging the
complementary strengths of them: Specialist Models handle simpler tasks
efficiently, while MLLMs are better suited for complex reasoning. Based on this
synergy, we introduce two collaborative strategies. The first, Slow-Fast
Adaptation (SFA), employs a routing mechanism to adaptively delegate simple
tasks to Specialist Models and complex tasks to MLLMs. Additionally, common
error patterns in both models are mitigated through a target-refocus strategy.
The second, Candidate Region Selection (CRS), generates multiple bounding box
candidates based on Specialist Model and uses the advanced reasoning
capabilities of MLLMs to identify the correct target. Extensive experiments on
our dataset and other challenging compositional benchmarks validate the
effectiveness of our approaches. The SFA strategy achieves a trade-off between
localization accuracy and efficiency, and the CRS strategy greatly boosts the
performance of both Specialist Models and MLLMs. We aim for this work to offer
valuable insights into solving complex real-world tasks by strategically
combining existing tools for maximum effectiveness, rather than reinventing
them.
|
no_new_dataset
| 0.831006
|
2502.20246
|
Chi Chien Tsai
|
Chi-Chien Tsai, Chia-Mu Yu, Ying-Dar Lin, Yu-Sung Wu, Wei-Bin Lee
|
Beyond Natural Language Perplexity: Detecting Dead Code Poisoning in
Code Generation Datasets
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The increasing adoption of large language models (LLMs) for code-related
tasks has raised concerns about the security of their training datasets. One
critical threat is dead code poisoning, where syntactically valid but
functionally redundant code is injected into training data to manipulate model
behavior. Such attacks can degrade the performance of neural code search
systems, leading to biased or insecure code suggestions. Existing detection
methods, such as token-level perplexity analysis, fail to effectively identify
dead code due to the structural and contextual characteristics of programming
languages. In this paper, we propose DePA (Dead Code Perplexity Analysis), a
novel line-level detection and cleansing method tailored to the structural
properties of code. DePA computes line-level perplexity by leveraging the
contextual relationships between code lines and identifies anomalous lines by
comparing their perplexity to the overall distribution within the file. Our
experiments on benchmark datasets demonstrate that DePA significantly
outperforms existing methods, achieving 0.14-0.19 improvement in detection
F1-score and a 44-65% increase in poisoned segment localization precision.
Furthermore, DePA enhances detection speed by 0.62-23x, making it practical for
large-scale dataset cleansing. Overall, by addressing the unique challenges of
dead code poisoning, DePA provides a robust and efficient solution for
safeguarding the integrity of code generation model training datasets.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 16:30:00 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 08:39:27 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Tsai",
"Chi-Chien",
""
],
[
"Yu",
"Chia-Mu",
""
],
[
"Lin",
"Ying-Dar",
""
],
[
"Wu",
"Yu-Sung",
""
],
[
"Lee",
"Wei-Bin",
""
]
] |
TITLE: Beyond Natural Language Perplexity: Detecting Dead Code Poisoning in
Code Generation Datasets
ABSTRACT: The increasing adoption of large language models (LLMs) for code-related
tasks has raised concerns about the security of their training datasets. One
critical threat is dead code poisoning, where syntactically valid but
functionally redundant code is injected into training data to manipulate model
behavior. Such attacks can degrade the performance of neural code search
systems, leading to biased or insecure code suggestions. Existing detection
methods, such as token-level perplexity analysis, fail to effectively identify
dead code due to the structural and contextual characteristics of programming
languages. In this paper, we propose DePA (Dead Code Perplexity Analysis), a
novel line-level detection and cleansing method tailored to the structural
properties of code. DePA computes line-level perplexity by leveraging the
contextual relationships between code lines and identifies anomalous lines by
comparing their perplexity to the overall distribution within the file. Our
experiments on benchmark datasets demonstrate that DePA significantly
outperforms existing methods, achieving 0.14-0.19 improvement in detection
F1-score and a 44-65% increase in poisoned segment localization precision.
Furthermore, DePA enhances detection speed by 0.62-23x, making it practical for
large-scale dataset cleansing. Overall, by addressing the unique challenges of
dead code poisoning, DePA provides a robust and efficient solution for
safeguarding the integrity of code generation model training datasets.
|
no_new_dataset
| 0.943243
|
2502.20272
|
Yixu Feng
|
Qingsen Yan, Yixu Feng, Cheng Zhang, Guansong Pang, Kangbiao Shi, Peng
Wu, Wei Dong, Jinqiu Sun, Yanning Zhang
|
HVI: A New Color Space for Low-light Image Enhancement
|
Qingsen Yan, Yixu Feng, and Cheng Zhang contributed equally to this
work
| null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Low-Light Image Enhancement (LLIE) is a crucial computer vision task that
aims to restore detailed visual information from corrupted low-light images.
Many existing LLIE methods are based on standard RGB (sRGB) space, which often
produce color bias and brightness artifacts due to inherent high color
sensitivity in sRGB. While converting the images using Hue, Saturation and
Value (HSV) color space helps resolve the brightness issue, it introduces
significant red and black noise artifacts. To address this issue, we propose a
new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined
by polarized HS maps and learnable intensity. The former enforces small
distances for red coordinates to remove the red artifacts, while the latter
compresses the low-light regions to remove the black artifacts. To fully
leverage the chromatic and intensity information, a novel Color and Intensity
Decoupling Network (CIDNet) is further introduced to learn accurate photometric
mapping function under different lighting conditions in the HVI space.
Comprehensive results from benchmark and ablation experiments show that the
proposed HVI color space with CIDNet outperforms the state-of-the-art methods
on 10 datasets. The code is available at https://github.com/Fediory/HVI-CIDNet.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 16:59:51 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Feb 2025 11:13:24 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Yan",
"Qingsen",
""
],
[
"Feng",
"Yixu",
""
],
[
"Zhang",
"Cheng",
""
],
[
"Pang",
"Guansong",
""
],
[
"Shi",
"Kangbiao",
""
],
[
"Wu",
"Peng",
""
],
[
"Dong",
"Wei",
""
],
[
"Sun",
"Jinqiu",
""
],
[
"Zhang",
"Yanning",
""
]
] |
TITLE: HVI: A New Color Space for Low-light Image Enhancement
ABSTRACT: Low-Light Image Enhancement (LLIE) is a crucial computer vision task that
aims to restore detailed visual information from corrupted low-light images.
Many existing LLIE methods are based on standard RGB (sRGB) space, which often
produce color bias and brightness artifacts due to inherent high color
sensitivity in sRGB. While converting the images using Hue, Saturation and
Value (HSV) color space helps resolve the brightness issue, it introduces
significant red and black noise artifacts. To address this issue, we propose a
new color space for LLIE, namely Horizontal/Vertical-Intensity (HVI), defined
by polarized HS maps and learnable intensity. The former enforces small
distances for red coordinates to remove the red artifacts, while the latter
compresses the low-light regions to remove the black artifacts. To fully
leverage the chromatic and intensity information, a novel Color and Intensity
Decoupling Network (CIDNet) is further introduced to learn accurate photometric
mapping function under different lighting conditions in the HVI space.
Comprehensive results from benchmark and ablation experiments show that the
proposed HVI color space with CIDNet outperforms the state-of-the-art methods
on 10 datasets. The code is available at https://github.com/Fediory/HVI-CIDNet.
|
no_new_dataset
| 0.953751
|
2502.20405
|
Yicheng Fu
|
James Begin, Namit Agrawal, Eshan Singh, Yicheng Fu, Sean O'Brien,
Vasu Sharma, Kevin Zhu
|
Pause-Tuning for Long-Context Comprehension: A Lightweight Approach to
LLM Attention Recalibration
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
LLMs have demonstrated remarkable proficiency in understanding tasks but
continue to struggle with long-context comprehension, particularly with content
located in the middle of extensive inputs. This limitation, known as the
Lost-in-the-Middle (LITM) problem, hinders models from fully processing and
utilizing information across lengthy contexts. To address this issue, we
introduce pause-tuning, a technique that redistributes attention to enhance
comprehension of long-context inputs. Our approach involves fine-tuning
language models on datasets with artificially inserted pause tokens, which
serve to segment the input into smaller, more manageable parts. We evaluate
pause-tuning against alternative approaches using the Needle-in-a-Haystack
benchmark, where models must retrieve information embedded within contexts of
up to 128K tokens. Experimental results demonstrate significant performance
gains, with the LLaMA 3.2 3B Instruct model and the LLaMA 3.1 8B Instruct model
improving by 10.61% and 3.57% respectively on average, suggesting that
pause-tuning successfully enhances attention redistribution and improves
long-context retention. The code and data are available at
https://anonymous.4open.science/r/LITM-PauseTokens-7357.
|
[
{
"version": "v1",
"created": "Sat, 1 Feb 2025 21:47:15 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Begin",
"James",
""
],
[
"Agrawal",
"Namit",
""
],
[
"Singh",
"Eshan",
""
],
[
"Fu",
"Yicheng",
""
],
[
"O'Brien",
"Sean",
""
],
[
"Sharma",
"Vasu",
""
],
[
"Zhu",
"Kevin",
""
]
] |
TITLE: Pause-Tuning for Long-Context Comprehension: A Lightweight Approach to
LLM Attention Recalibration
ABSTRACT: LLMs have demonstrated remarkable proficiency in understanding tasks but
continue to struggle with long-context comprehension, particularly with content
located in the middle of extensive inputs. This limitation, known as the
Lost-in-the-Middle (LITM) problem, hinders models from fully processing and
utilizing information across lengthy contexts. To address this issue, we
introduce pause-tuning, a technique that redistributes attention to enhance
comprehension of long-context inputs. Our approach involves fine-tuning
language models on datasets with artificially inserted pause tokens, which
serve to segment the input into smaller, more manageable parts. We evaluate
pause-tuning against alternative approaches using the Needle-in-a-Haystack
benchmark, where models must retrieve information embedded within contexts of
up to 128K tokens. Experimental results demonstrate significant performance
gains, with the LLaMA 3.2 3B Instruct model and the LLaMA 3.1 8B Instruct model
improving by 10.61% and 3.57% respectively on average, suggesting that
pause-tuning successfully enhances attention redistribution and improves
long-context retention. The code and data are available at
https://anonymous.4open.science/r/LITM-PauseTokens-7357.
|
no_new_dataset
| 0.947721
|
2502.20411
|
Saeed Reza Kheradpisheh
|
Mohammadnavid Ghader, Saeed Reza Kheradpisheh, Bahar Farahani, Mahmood
Fazlali
|
Backpropagation-free Spiking Neural Networks with the Forward-Forward
Algorithm
| null | null | null | null |
cs.NE cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Spiking Neural Networks (SNNs) offer a biologically inspired computational
paradigm that emulates neuronal activity through discrete spike-based
processing. Despite their advantages, training SNNs with traditional
backpropagation (BP) remains challenging due to computational inefficiencies
and a lack of biological plausibility. This study explores the Forward-Forward
(FF) algorithm as an alternative learning framework for SNNs. Unlike
backpropagation, which relies on forward and backward passes, the FF algorithm
employs two forward passes, enabling localized learning, enhanced computational
efficiency, and improved compatibility with neuromorphic hardware. We introduce
an FF-based SNN training framework and evaluate its performance across both
non-spiking (MNIST, Fashion-MNIST, CIFAR-10) and spiking (Neuro-MNIST, SHD)
datasets. Experimental results demonstrate that our model surpasses existing
FF-based SNNs by over 5% on MNIST and Fashion-MNIST while achieving accuracy
comparable to state-of-the-art backpropagation-trained SNNs. On more complex
tasks such as CIFAR-10 and SHD, our approach outperforms other SNN models by up
to 6% and remains competitive with leading backpropagation-trained SNNs. These
findings highlight the FF algorithm's potential to advance SNN training
methodologies and neuromorphic computing by addressing key limitations of
backpropagation.
|
[
{
"version": "v1",
"created": "Wed, 19 Feb 2025 12:44:26 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Ghader",
"Mohammadnavid",
""
],
[
"Kheradpisheh",
"Saeed Reza",
""
],
[
"Farahani",
"Bahar",
""
],
[
"Fazlali",
"Mahmood",
""
]
] |
TITLE: Backpropagation-free Spiking Neural Networks with the Forward-Forward
Algorithm
ABSTRACT: Spiking Neural Networks (SNNs) offer a biologically inspired computational
paradigm that emulates neuronal activity through discrete spike-based
processing. Despite their advantages, training SNNs with traditional
backpropagation (BP) remains challenging due to computational inefficiencies
and a lack of biological plausibility. This study explores the Forward-Forward
(FF) algorithm as an alternative learning framework for SNNs. Unlike
backpropagation, which relies on forward and backward passes, the FF algorithm
employs two forward passes, enabling localized learning, enhanced computational
efficiency, and improved compatibility with neuromorphic hardware. We introduce
an FF-based SNN training framework and evaluate its performance across both
non-spiking (MNIST, Fashion-MNIST, CIFAR-10) and spiking (Neuro-MNIST, SHD)
datasets. Experimental results demonstrate that our model surpasses existing
FF-based SNNs by over 5% on MNIST and Fashion-MNIST while achieving accuracy
comparable to state-of-the-art backpropagation-trained SNNs. On more complex
tasks such as CIFAR-10 and SHD, our approach outperforms other SNN models by up
to 6% and remains competitive with leading backpropagation-trained SNNs. These
findings highlight the FF algorithm's potential to advance SNN training
methodologies and neuromorphic computing by addressing key limitations of
backpropagation.
|
no_new_dataset
| 0.948442
|
2502.20422
|
Zicheng Cai
|
Zicheng Cai, Yaohua Tang, Yutao Lai, Hua Wang, Zhi Chen and Hao Chen
|
SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture
Search via Large Language Models
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce SEKI, a novel large language model (LLM)-based neural
architecture search (NAS) method. Inspired by the chain-of-thought (CoT)
paradigm in modern LLMs, SEKI operates in two key stages: self-evolution and
knowledge distillation. In the self-evolution stage, LLMs initially lack
sufficient reference examples, so we implement an iterative refinement
mechanism that enhances architectures based on performance feedback. Over time,
this process accumulates a repository of high-performance architectures. In the
knowledge distillation stage, LLMs analyze common patterns among these
architectures to generate new, optimized designs. Combining these two stages,
SEKI greatly leverages the capacity of LLMs on NAS and without requiring any
domain-specific data. Experimental results show that SEKI achieves
state-of-the-art (SOTA) performance across various datasets and search spaces
while requiring only 0.05 GPU-days, outperforming existing methods in both
efficiency and accuracy. Furthermore, SEKI demonstrates strong generalization
capabilities, achieving SOTA-competitive results across multiple tasks.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 09:17:49 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Cai",
"Zicheng",
""
],
[
"Tang",
"Yaohua",
""
],
[
"Lai",
"Yutao",
""
],
[
"Wang",
"Hua",
""
],
[
"Chen",
"Zhi",
""
],
[
"Chen",
"Hao",
""
]
] |
TITLE: SEKI: Self-Evolution and Knowledge Inspiration based Neural Architecture
Search via Large Language Models
ABSTRACT: We introduce SEKI, a novel large language model (LLM)-based neural
architecture search (NAS) method. Inspired by the chain-of-thought (CoT)
paradigm in modern LLMs, SEKI operates in two key stages: self-evolution and
knowledge distillation. In the self-evolution stage, LLMs initially lack
sufficient reference examples, so we implement an iterative refinement
mechanism that enhances architectures based on performance feedback. Over time,
this process accumulates a repository of high-performance architectures. In the
knowledge distillation stage, LLMs analyze common patterns among these
architectures to generate new, optimized designs. Combining these two stages,
SEKI greatly leverages the capacity of LLMs on NAS and without requiring any
domain-specific data. Experimental results show that SEKI achieves
state-of-the-art (SOTA) performance across various datasets and search spaces
while requiring only 0.05 GPU-days, outperforming existing methods in both
efficiency and accuracy. Furthermore, SEKI demonstrates strong generalization
capabilities, achieving SOTA-competitive results across multiple tasks.
|
no_new_dataset
| 0.942029
|
2502.20480
|
Chaoyu Li
|
Chaoyu Li, Sid Padmanabhuni, Maryam Cheema, Hasti Seifi, Pooyan Fazli
|
VideoA11y: Method and Dataset for Accessible Video Description
|
ACM CHI 2025
| null | null | null |
cs.CV cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Video descriptions are crucial for blind and low vision (BLV) users to access
visual content. However, current artificial intelligence models for generating
descriptions often fall short due to limitations in the quality of human
annotations within training datasets, resulting in descriptions that do not
fully meet BLV users' needs. To address this gap, we introduce VideoA11y, an
approach that leverages multimodal large language models (MLLMs) and video
accessibility guidelines to generate descriptions tailored for BLV individuals.
Using this method, we have curated VideoA11y-40K, the largest and most
comprehensive dataset of 40,000 videos described for BLV users. Rigorous
experiments across 15 video categories, involving 347 sighted participants, 40
BLV participants, and seven professional describers, showed that VideoA11y
descriptions outperform novice human annotations and are comparable to trained
human annotations in clarity, accuracy, objectivity, descriptiveness, and user
satisfaction. We evaluated models on VideoA11y-40K using both standard and
custom metrics, demonstrating that MLLMs fine-tuned on this dataset produce
high-quality accessible descriptions. Code and dataset are available at
https://people-robots.github.io/VideoA11y.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 19:44:31 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Li",
"Chaoyu",
""
],
[
"Padmanabhuni",
"Sid",
""
],
[
"Cheema",
"Maryam",
""
],
[
"Seifi",
"Hasti",
""
],
[
"Fazli",
"Pooyan",
""
]
] |
TITLE: VideoA11y: Method and Dataset for Accessible Video Description
ABSTRACT: Video descriptions are crucial for blind and low vision (BLV) users to access
visual content. However, current artificial intelligence models for generating
descriptions often fall short due to limitations in the quality of human
annotations within training datasets, resulting in descriptions that do not
fully meet BLV users' needs. To address this gap, we introduce VideoA11y, an
approach that leverages multimodal large language models (MLLMs) and video
accessibility guidelines to generate descriptions tailored for BLV individuals.
Using this method, we have curated VideoA11y-40K, the largest and most
comprehensive dataset of 40,000 videos described for BLV users. Rigorous
experiments across 15 video categories, involving 347 sighted participants, 40
BLV participants, and seven professional describers, showed that VideoA11y
descriptions outperform novice human annotations and are comparable to trained
human annotations in clarity, accuracy, objectivity, descriptiveness, and user
satisfaction. We evaluated models on VideoA11y-40K using both standard and
custom metrics, demonstrating that MLLMs fine-tuned on this dataset produce
high-quality accessible descriptions. Code and dataset are available at
https://people-robots.github.io/VideoA11y.
|
new_dataset
| 0.961134
|
2502.20493
|
Vijay Srinivas Tida
|
Vijay Srinivas Tida, Md Imran Hossen, Liqun Shan, Sai Venkatesh
Chilukoti, Sonya Hsu, Xiali Hei
|
Unified Kernel-Segregated Transpose Convolution Operation
| null | null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
The optimization of the transpose convolution layer for deep learning
applications is achieved with the kernel segregation mechanism. However, kernel
segregation has disadvantages, such as computing extra elements to obtain the
output feature map with odd dimensions while launching a thread. To mitigate
this problem, we introduce a unified kernel segregation approach that limits
the usage of memory and computational resources by employing one unified kernel
to execute four sub-kernels. The findings reveal that the suggested approach
achieves an average computational speedup of 2.03x (3.89x) when tested on
specific datasets with an RTX 2070 GPU (Intel Xeon CPU). The ablation study
shows an average computational speedup of 3.5x when evaluating the transpose
convolution layers from well-known Generative Adversarial Networks (GANs). The
implementation of the proposed method for the transpose convolution layers in
the EB-GAN model demonstrates significant memory savings of up to 35 MB.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 19:56:25 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Tida",
"Vijay Srinivas",
""
],
[
"Hossen",
"Md Imran",
""
],
[
"Shan",
"Liqun",
""
],
[
"Chilukoti",
"Sai Venkatesh",
""
],
[
"Hsu",
"Sonya",
""
],
[
"Hei",
"Xiali",
""
]
] |
TITLE: Unified Kernel-Segregated Transpose Convolution Operation
ABSTRACT: The optimization of the transpose convolution layer for deep learning
applications is achieved with the kernel segregation mechanism. However, kernel
segregation has disadvantages, such as computing extra elements to obtain the
output feature map with odd dimensions while launching a thread. To mitigate
this problem, we introduce a unified kernel segregation approach that limits
the usage of memory and computational resources by employing one unified kernel
to execute four sub-kernels. The findings reveal that the suggested approach
achieves an average computational speedup of 2.03x (3.89x) when tested on
specific datasets with an RTX 2070 GPU (Intel Xeon CPU). The ablation study
shows an average computational speedup of 3.5x when evaluating the transpose
convolution layers from well-known Generative Adversarial Networks (GANs). The
implementation of the proposed method for the transpose convolution layers in
the EB-GAN model demonstrates significant memory savings of up to 35 MB.
|
no_new_dataset
| 0.949342
|
2502.20504
|
Julius Broomfield
|
Julius Broomfield, Kartik Sharma, Srijan Kumar
|
A Thousand Words or An Image: Studying the Influence of Persona Modality
in Multimodal LLMs
| null | null | null | null |
cs.CL cs.AI cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Large language models (LLMs) have recently demonstrated remarkable
advancements in embodying diverse personas, enhancing their effectiveness as
conversational agents and virtual assistants. Consequently, LLMs have made
significant strides in processing and integrating multimodal information.
However, even though human personas can be expressed in both text and image,
the extent to which the modality of a persona impacts the embodiment by the LLM
remains largely unexplored. In this paper, we investigate how do different
modalities influence the expressiveness of personas in multimodal LLMs. To this
end, we create a novel modality-parallel dataset of 40 diverse personas varying
in age, gender, occupation, and location. This consists of four modalities to
equivalently represent a persona: image-only, text-only, a combination of image
and small text, and typographical images, where text is visually stylized to
convey persona-related attributes. We then create a systematic evaluation
framework with 60 questions and corresponding metrics to assess how well LLMs
embody each persona across its attributes and scenarios. Comprehensive
experiments on $5$ multimodal LLMs show that personas represented by detailed
text show more linguistic habits, while typographical images often show more
consistency with the persona. Our results reveal that LLMs often overlook
persona-specific details conveyed through images, highlighting underlying
limitations and paving the way for future research to bridge this gap. We
release the data and code at https://github.com/claws-lab/persona-modality .
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 20:25:00 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Broomfield",
"Julius",
""
],
[
"Sharma",
"Kartik",
""
],
[
"Kumar",
"Srijan",
""
]
] |
TITLE: A Thousand Words or An Image: Studying the Influence of Persona Modality
in Multimodal LLMs
ABSTRACT: Large language models (LLMs) have recently demonstrated remarkable
advancements in embodying diverse personas, enhancing their effectiveness as
conversational agents and virtual assistants. Consequently, LLMs have made
significant strides in processing and integrating multimodal information.
However, even though human personas can be expressed in both text and image,
the extent to which the modality of a persona impacts the embodiment by the LLM
remains largely unexplored. In this paper, we investigate how do different
modalities influence the expressiveness of personas in multimodal LLMs. To this
end, we create a novel modality-parallel dataset of 40 diverse personas varying
in age, gender, occupation, and location. This consists of four modalities to
equivalently represent a persona: image-only, text-only, a combination of image
and small text, and typographical images, where text is visually stylized to
convey persona-related attributes. We then create a systematic evaluation
framework with 60 questions and corresponding metrics to assess how well LLMs
embody each persona across its attributes and scenarios. Comprehensive
experiments on $5$ multimodal LLMs show that personas represented by detailed
text show more linguistic habits, while typographical images often show more
consistency with the persona. Our results reveal that LLMs often overlook
persona-specific details conveyed through images, highlighting underlying
limitations and paving the way for future research to bridge this gap. We
release the data and code at https://github.com/claws-lab/persona-modality .
|
new_dataset
| 0.961929
|
2502.20508
|
Soumyabrata Chaudhuri
|
Soumyabrata Chaudhuri, Pranav Purkar, Ritwik Raghav, Shubhojit
Mallick, Manish Gupta, Abhik Jana, Shreya Ghosh
|
TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel
Planning
|
27 pages, 18 Tables and 6 Figures
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Recent advancements in probing Large Language Models (LLMs) have explored
their latent potential as personalized travel planning agents, yet existing
benchmarks remain limited in real world applicability. Existing datasets, such
as TravelPlanner and TravelPlanner+, suffer from semi synthetic data reliance,
spatial inconsistencies, and a lack of key travel constraints, making them
inadequate for practical itinerary generation. To address these gaps, we
introduce TripCraft, a spatiotemporally coherent travel planning dataset that
integrates real world constraints, including public transit schedules, event
availability, diverse attraction categories, and user personas for enhanced
personalization. To evaluate LLM generated plans beyond existing binary
validation methods, we propose five continuous evaluation metrics, namely
Temporal Meal Score, Temporal Attraction Score, Spatial Score, Ordering Score,
and Persona Score which assess itinerary quality across multiple dimensions.
Our parameter informed setting significantly enhances meal scheduling,
improving the Temporal Meal Score from 61% to 80% in a 7 day scenario.
TripCraft establishes a new benchmark for LLM driven personalized travel
planning, offering a more realistic, constraint aware framework for itinerary
generation. Dataset and Codebase will be made publicly available upon
acceptance.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 20:33:28 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Chaudhuri",
"Soumyabrata",
""
],
[
"Purkar",
"Pranav",
""
],
[
"Raghav",
"Ritwik",
""
],
[
"Mallick",
"Shubhojit",
""
],
[
"Gupta",
"Manish",
""
],
[
"Jana",
"Abhik",
""
],
[
"Ghosh",
"Shreya",
""
]
] |
TITLE: TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel
Planning
ABSTRACT: Recent advancements in probing Large Language Models (LLMs) have explored
their latent potential as personalized travel planning agents, yet existing
benchmarks remain limited in real world applicability. Existing datasets, such
as TravelPlanner and TravelPlanner+, suffer from semi synthetic data reliance,
spatial inconsistencies, and a lack of key travel constraints, making them
inadequate for practical itinerary generation. To address these gaps, we
introduce TripCraft, a spatiotemporally coherent travel planning dataset that
integrates real world constraints, including public transit schedules, event
availability, diverse attraction categories, and user personas for enhanced
personalization. To evaluate LLM generated plans beyond existing binary
validation methods, we propose five continuous evaluation metrics, namely
Temporal Meal Score, Temporal Attraction Score, Spatial Score, Ordering Score,
and Persona Score which assess itinerary quality across multiple dimensions.
Our parameter informed setting significantly enhances meal scheduling,
improving the Temporal Meal Score from 61% to 80% in a 7 day scenario.
TripCraft establishes a new benchmark for LLM driven personalized travel
planning, offering a more realistic, constraint aware framework for itinerary
generation. Dataset and Codebase will be made publicly available upon
acceptance.
|
new_dataset
| 0.958265
|
2502.20513
|
Smit Desai
|
Smit Desai, Mateusz Dubiel, Nima Zargham, Thomas Mildner, Laura
Spillner
|
Personas Evolved: Designing Ethical LLM-Based Conversational Agent
Personalities
| null | null | null | null |
cs.HC cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The emergence of Large Language Models (LLMs) has revolutionized
Conversational User Interfaces (CUIs), enabling more dynamic, context-aware,
and human-like interactions across diverse domains, from social sciences to
healthcare. However, the rapid adoption of LLM-based personas raises critical
ethical and practical concerns, including bias, manipulation, and unforeseen
social consequences. Unlike traditional CUIs, where personas are carefully
designed with clear intent, LLM-based personas generate responses dynamically
from vast datasets, making their behavior less predictable and harder to
govern. This workshop aims to bridge the gap between CUI and broader AI
communities by fostering a cross-disciplinary dialogue on the responsible
design and evaluation of LLM-based personas. Bringing together researchers,
designers, and practitioners, we will explore best practices, develop ethical
guidelines, and promote frameworks that ensure transparency, inclusivity, and
user-centered interactions. By addressing these challenges collaboratively, we
seek to shape the future of LLM-driven CUIs in ways that align with societal
values and expectations.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 20:46:54 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Desai",
"Smit",
""
],
[
"Dubiel",
"Mateusz",
""
],
[
"Zargham",
"Nima",
""
],
[
"Mildner",
"Thomas",
""
],
[
"Spillner",
"Laura",
""
]
] |
TITLE: Personas Evolved: Designing Ethical LLM-Based Conversational Agent
Personalities
ABSTRACT: The emergence of Large Language Models (LLMs) has revolutionized
Conversational User Interfaces (CUIs), enabling more dynamic, context-aware,
and human-like interactions across diverse domains, from social sciences to
healthcare. However, the rapid adoption of LLM-based personas raises critical
ethical and practical concerns, including bias, manipulation, and unforeseen
social consequences. Unlike traditional CUIs, where personas are carefully
designed with clear intent, LLM-based personas generate responses dynamically
from vast datasets, making their behavior less predictable and harder to
govern. This workshop aims to bridge the gap between CUI and broader AI
communities by fostering a cross-disciplinary dialogue on the responsible
design and evaluation of LLM-based personas. Bringing together researchers,
designers, and practitioners, we will explore best practices, develop ethical
guidelines, and promote frameworks that ensure transparency, inclusivity, and
user-centered interactions. By addressing these challenges collaboratively, we
seek to shape the future of LLM-driven CUIs in ways that align with societal
values and expectations.
|
no_new_dataset
| 0.94801
|
2502.20516
|
Hu Wang
|
Hu Wang, Ibrahim Almakky, Congbo Ma, Numan Saeed, Mohammad Yaqub
|
In-Model Merging for Enhancing the Robustness of Medical Imaging
Classification Models
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Model merging is an effective strategy to merge multiple models for enhancing
model performances, and more efficient than ensemble learning as it will not
introduce extra computation into inference. However, limited research explores
if the merging process can occur within one model and enhance the model's
robustness, which is particularly critical in the medical image domain. In the
paper, we are the first to propose in-model merging (InMerge), a novel approach
that enhances the model's robustness by selectively merging similar
convolutional kernels in the deep layers of a single convolutional neural
network (CNN) during the training process for classification. We also
analytically reveal important characteristics that affect how in-model merging
should be performed, serving as an insightful reference for the community. We
demonstrate the feasibility and effectiveness of this technique for different
CNN architectures on 4 prevalent datasets. The proposed InMerge-trained model
surpasses the typically-trained model by a substantial margin. The code will be
made public.
|
[
{
"version": "v1",
"created": "Thu, 27 Feb 2025 20:52:55 GMT"
}
] | 2025-03-03T00:00:00
|
[
[
"Wang",
"Hu",
""
],
[
"Almakky",
"Ibrahim",
""
],
[
"Ma",
"Congbo",
""
],
[
"Saeed",
"Numan",
""
],
[
"Yaqub",
"Mohammad",
""
]
] |
TITLE: In-Model Merging for Enhancing the Robustness of Medical Imaging
Classification Models
ABSTRACT: Model merging is an effective strategy to merge multiple models for enhancing
model performances, and more efficient than ensemble learning as it will not
introduce extra computation into inference. However, limited research explores
if the merging process can occur within one model and enhance the model's
robustness, which is particularly critical in the medical image domain. In the
paper, we are the first to propose in-model merging (InMerge), a novel approach
that enhances the model's robustness by selectively merging similar
convolutional kernels in the deep layers of a single convolutional neural
network (CNN) during the training process for classification. We also
analytically reveal important characteristics that affect how in-model merging
should be performed, serving as an insightful reference for the community. We
demonstrate the feasibility and effectiveness of this technique for different
CNN architectures on 4 prevalent datasets. The proposed InMerge-trained model
surpasses the typically-trained model by a substantial margin. The code will be
made public.
|
no_new_dataset
| 0.951278
|
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