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https://aclanthology.org/2024.gebnlp-1.5.bib
|
@inproceedings{baghel-etal-2024-fairness,
title = "A Fairness Analysis of Human and {AI}-Generated Student Reflection Summaries",
author = "Baghel, Bhiman and
Lekshmi Narayanan, Arun Balajiee and
Yoder, Michael",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.5",
pages = "60--77",
abstract = "This study examines the fairness of human- and AI-generated summaries of student reflections in university STEM classes, focusing on potential gender biases. Using topic modeling, we first identify topics that are more prevalent in reflections from female students and others that are more common among male students. We then analyze whether human and AI-generated summaries reflect the concerns of students of any particular gender over others. Our analysis reveals that though human-generated and extractive AI summarization techniques do not show a clear bias, abstractive AI-generated summaries exhibit a bias towards male students. Pedagogical themes are over-represented from male reflections in these summaries, while concept-specific topics are under-represented from female reflections. This research contributes to a deeper understanding of AI-generated bias in educational contexts, highlighting the need for future work on mitigating these biases.",
}
|
This study examines the fairness of human- and AI-generated summaries of student reflections in university STEM classes, focusing on potential gender biases. Using topic modeling, we first identify topics that are more prevalent in reflections from female students and others that are more common among male students. We then analyze whether human and AI-generated summaries reflect the concerns of students of any particular gender over others. Our analysis reveals that though human-generated and extractive AI summarization techniques do not show a clear bias, abstractive AI-generated summaries exhibit a bias towards male students. Pedagogical themes are over-represented from male reflections in these summaries, while concept-specific topics are under-represented from female reflections. This research contributes to a deeper understanding of AI-generated bias in educational contexts, highlighting the need for future work on mitigating these biases.
|
[
"Baghel, Bhiman",
"Lekshmi Narayanan, Arun Balajiee",
"Yoder, Michael"
] |
A Fairness Analysis of Human and {AI}-Generated Student Reflection Summaries
|
gebnlp-1.5
|
Poster
|
2002.03407v1
|
https://aclanthology.org/2024.gebnlp-1.6.bib
|
@inproceedings{fanton-roth-2024-shortcuts,
title = "On Shortcuts and Biases: How Finetuned Language Models Distinguish Audience-Specific Instructions in {I}talian and {E}nglish",
author = "Fanton, Nicola and
Roth, Michael",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.6",
pages = "78--93",
abstract = "Instructional texts for different audience groups can help to address specific needs, but at the same time run the risk of perpetrating biases. In this paper, we extend previous findings on disparate social norms and subtle stereotypes in wikiHow in two directions: We explore the use of fine-tuned language models to determine how audience-specific instructional texts can be distinguished and we transfer the methodology to another language, Italian, to identify cross-linguistic patterns. We find that language models mostly rely on group terms, gender markings, and attributes reinforcing stereotypes.",
}
|
Instructional texts for different audience groups can help to address specific needs, but at the same time run the risk of perpetrating biases. In this paper, we extend previous findings on disparate social norms and subtle stereotypes in wikiHow in two directions: We explore the use of fine-tuned language models to determine how audience-specific instructional texts can be distinguished and we transfer the methodology to another language, Italian, to identify cross-linguistic patterns. We find that language models mostly rely on group terms, gender markings, and attributes reinforcing stereotypes.
|
[
"Fanton, Nicola",
"Roth, Michael"
] |
On Shortcuts and Biases: How Finetuned Language Models Distinguish Audience-Specific Instructions in {I}talian and {E}nglish
|
gebnlp-1.6
|
Poster
|
2307.16456v2
|
https://aclanthology.org/2024.gebnlp-1.7.bib
|
@inproceedings{sant-etal-2024-power,
title = "The power of Prompts: Evaluating and Mitigating Gender Bias in {MT} with {LLM}s",
author = "Sant, Aleix and
Escolano, Carlos and
Mash, Audrey and
De Luca Fornaciari, Francesca and
Melero, Maite",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.7",
pages = "94--139",
abstract = "This paper studies gender bias in machine translation through the lens of Large Language Models (LLMs). Four widely-used test sets are employed to benchmark various base LLMs, comparing their translation quality and gender bias against state-of-the-art Neural Machine Translation (NMT) models for English to Catalan (En â Ca) and English to Spanish (En â Es) translation directions. Our findings reveal pervasive gender bias across all models, with base LLMs exhibiting a higher degree of bias compared to NMT models.To combat this bias, we explore prompting engineering techniques applied to an instruction-tuned LLM. We identify a prompt structure that significantly reduces gender bias by up to 12{\%} on the WinoMT evaluation dataset compared to more straightforward prompts. These results significantly reduce the gender bias accuracy gap between LLMs and traditional NMT systems.",
}
|
This paper studies gender bias in machine translation through the lens of Large Language Models (LLMs). Four widely-used test sets are employed to benchmark various base LLMs, comparing their translation quality and gender bias against state-of-the-art Neural Machine Translation (NMT) models for English to Catalan (En â Ca) and English to Spanish (En â Es) translation directions. Our findings reveal pervasive gender bias across all models, with base LLMs exhibiting a higher degree of bias compared to NMT models.To combat this bias, we explore prompting engineering techniques applied to an instruction-tuned LLM. We identify a prompt structure that significantly reduces gender bias by up to 12{\%} on the WinoMT evaluation dataset compared to more straightforward prompts. These results significantly reduce the gender bias accuracy gap between LLMs and traditional NMT systems.
|
[
"Sant, Aleix",
"Escolano, Carlos",
"Mash, Audrey",
"De Luca Fornaciari, Francesca",
"Melero, Maite"
] |
The power of Prompts: Evaluating and Mitigating Gender Bias in {MT} with {LLM}s
|
gebnlp-1.7
|
Poster
|
2407.18786v1
|
https://aclanthology.org/2024.gebnlp-1.8.bib
|
@inproceedings{urchs-etal-2024-detecting,
title = "Detecting Gender Discrimination on Actor Level Using Linguistic Discourse Analysis",
author = "Urchs, Stefanie and
Thurner, Veronika and
A{\ss}enmacher, Matthias and
Heumann, Christian and
Thiemichen, Stephanie",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.8",
pages = "140--149",
abstract = "With the usage of tremendous amounts of text data for training powerful large language models such as ChatGPT, the issue of analysing and securing data quality has become more pressing than ever. Any biases, stereotypes and discriminatory patterns that exist in the training data can be reproduced, reinforced or broadly disseminated by the models in production. Therefore, it is crucial to carefully select and monitor the text data that is used as input to train the model. Due to the vast amount of training data, this process needs to be (at least partially) automated. In this work, we introduce a novel approach for automatically detecting gender discrimination in text data on the actor level based on linguistic discourse analysis. Specifically, we combine existing information extraction (IE) techniques to partly automate the qualitative research done in linguistic discourse analysis. We focus on two important steps: Identifying the respectiveperson-named-entity (an actor) and all forms it is referred to (Nomination), and detecting the characteristics it is ascribed (Predication). Asa proof of concept, we integrate these two steps into a pipeline for automated text analysis. The separate building blocks of the pipeline could be flexibly adapted, extended, and scaled for bigger datasets to accommodate a wide range of usage scenarios and specific ML tasks or help social scientists with analysis tasks. We showcase and evaluate our approach on several real and simulated exemplary texts.",
}
|
With the usage of tremendous amounts of text data for training powerful large language models such as ChatGPT, the issue of analysing and securing data quality has become more pressing than ever. Any biases, stereotypes and discriminatory patterns that exist in the training data can be reproduced, reinforced or broadly disseminated by the models in production. Therefore, it is crucial to carefully select and monitor the text data that is used as input to train the model. Due to the vast amount of training data, this process needs to be (at least partially) automated. In this work, we introduce a novel approach for automatically detecting gender discrimination in text data on the actor level based on linguistic discourse analysis. Specifically, we combine existing information extraction (IE) techniques to partly automate the qualitative research done in linguistic discourse analysis. We focus on two important steps: Identifying the respectiveperson-named-entity (an actor) and all forms it is referred to (Nomination), and detecting the characteristics it is ascribed (Predication). Asa proof of concept, we integrate these two steps into a pipeline for automated text analysis. The separate building blocks of the pipeline could be flexibly adapted, extended, and scaled for bigger datasets to accommodate a wide range of usage scenarios and specific ML tasks or help social scientists with analysis tasks. We showcase and evaluate our approach on several real and simulated exemplary texts.
|
[
"Urchs, Stefanie",
"Thurner, Veronika",
"A{\\ss}enmacher, Matthias",
"Heumann, Christian",
"Thiemichen, Stephanie"
] |
Detecting Gender Discrimination on Actor Level Using Linguistic Discourse Analysis
|
gebnlp-1.8
|
Poster
|
1906.00742v1
|
https://aclanthology.org/2024.gebnlp-1.9.bib
|
@inproceedings{chen-etal-2024-go,
title = "What Can Go Wrong in Authorship Profiling: Cross-Domain Analysis of Gender and Age Prediction",
author = "Chen, Hongyu and
Roth, Michael and
Falenska, Agnieszka",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.9",
pages = "150--166",
abstract = "Authorship Profiling (AP) aims to predict the demographic attributes (such as gender and age) of authors based on their writing styles. Ever-improving models mean that this task is gaining interest and application possibilities. However, with greater use also comes the risk that authors are misclassified more frequently, and it remains unclear to what extent the better models can capture the bias and who is affected by the models{'} mistakes. In this paper, we investigate three established datasets for AP as well as classical and neural classifiers for this task. Our analyses show that it is often possible to predict the demographic information of the authors based on textual features. However, some features learned by the models are specific to datasets. Moreover, models are prone to errors based on stereotypes associated with topical bias.",
}
|
Authorship Profiling (AP) aims to predict the demographic attributes (such as gender and age) of authors based on their writing styles. Ever-improving models mean that this task is gaining interest and application possibilities. However, with greater use also comes the risk that authors are misclassified more frequently, and it remains unclear to what extent the better models can capture the bias and who is affected by the models{'} mistakes. In this paper, we investigate three established datasets for AP as well as classical and neural classifiers for this task. Our analyses show that it is often possible to predict the demographic information of the authors based on textual features. However, some features learned by the models are specific to datasets. Moreover, models are prone to errors based on stereotypes associated with topical bias.
|
[
"Chen, Hongyu",
"Roth, Michael",
"Falenska, Agnieszka"
] |
What Can Go Wrong in Authorship Profiling: Cross-Domain Analysis of Gender and Age Prediction
|
gebnlp-1.9
|
Poster
|
2102.03692v1
|
https://aclanthology.org/2024.gebnlp-1.10.bib
|
@inproceedings{sobhani-delany-2024-towards,
title = "Towards Fairer {NLP} Models: Handling Gender Bias In Classification Tasks",
author = "Sobhani, Nasim and
Delany, Sarah",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.10",
pages = "167--178",
abstract = "Measuring and mitigating gender bias in natural language processing (NLP) systems is crucial to ensure fair and ethical AI. However, a key challenge is the lack of explicit gender information in many textual datasets. This paper proposes two techniques, Identity Term Sampling (ITS) and Identity Term Pattern Extraction (ITPE), as alternatives to template-based approaches for measuring gender bias in text data. These approaches identify test data for measuring gender bias in the dataset itself and can be used to measure gender bias on any NLP classifier. We demonstrate the use of these approaches for measuring gender bias across various NLP classification tasks, including hate speech detection, fake news identification, and sentiment analysis. Additionally, we show how these techniques can benefit gender bias mitigation, proposing a variant of Counterfactual Data Augmentation (CDA), called Gender-Selective CDA (GS-CDA), which reduces the amount of data augmentation required in training data while effectively mitigating gender bias and maintaining overall classification performance.",
}
|
Measuring and mitigating gender bias in natural language processing (NLP) systems is crucial to ensure fair and ethical AI. However, a key challenge is the lack of explicit gender information in many textual datasets. This paper proposes two techniques, Identity Term Sampling (ITS) and Identity Term Pattern Extraction (ITPE), as alternatives to template-based approaches for measuring gender bias in text data. These approaches identify test data for measuring gender bias in the dataset itself and can be used to measure gender bias on any NLP classifier. We demonstrate the use of these approaches for measuring gender bias across various NLP classification tasks, including hate speech detection, fake news identification, and sentiment analysis. Additionally, we show how these techniques can benefit gender bias mitigation, proposing a variant of Counterfactual Data Augmentation (CDA), called Gender-Selective CDA (GS-CDA), which reduces the amount of data augmentation required in training data while effectively mitigating gender bias and maintaining overall classification performance.
|
[
"Sobhani, Nasim",
"Delany, Sarah"
] |
Towards Fairer {NLP} Models: Handling Gender Bias In Classification Tasks
|
gebnlp-1.10
|
Poster
|
2310.12127v2
|
https://aclanthology.org/2024.gebnlp-1.11.bib
|
@inproceedings{dikshit-etal-2024-investigating,
title = "Investigating Gender Bias in {STEM} Job Advertisements",
author = "Dikshit, Malika and
Bouamor, Houda and
Habash, Nizar",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.11",
pages = "179--189",
abstract = "Gender inequality has been historically prevalent in academia, especially within the fields of Science, Technology, Engineering, and Mathematics (STEM). In this study, we propose to examine gender bias in academic job descriptions in the STEM fields. We go a step further than previous studies that merely identify individual words as masculine-coded and feminine-coded and delve into the contextual language used in academic job advertisements. We design a novel approach to detect gender biases in job descriptions using Natural Language Processing techniques. Going beyond binary masculine-feminine stereotypes, we propose three big group types to understand gender bias in the language of job descriptions, namely agentic, balanced, and communal. We cluster similar information in job descriptions into these three groups using contrastive learning and various clustering techniques. This research contributes to the field of gender bias detection by providing a novel approach and methodology for categorizing gender bias in job descriptions, which can aid more effective and targeted job advertisements that will be equally appealing across all genders.",
}
|
Gender inequality has been historically prevalent in academia, especially within the fields of Science, Technology, Engineering, and Mathematics (STEM). In this study, we propose to examine gender bias in academic job descriptions in the STEM fields. We go a step further than previous studies that merely identify individual words as masculine-coded and feminine-coded and delve into the contextual language used in academic job advertisements. We design a novel approach to detect gender biases in job descriptions using Natural Language Processing techniques. Going beyond binary masculine-feminine stereotypes, we propose three big group types to understand gender bias in the language of job descriptions, namely agentic, balanced, and communal. We cluster similar information in job descriptions into these three groups using contrastive learning and various clustering techniques. This research contributes to the field of gender bias detection by providing a novel approach and methodology for categorizing gender bias in job descriptions, which can aid more effective and targeted job advertisements that will be equally appealing across all genders.
|
[
"Dikshit, Malika",
"Bouamor, Houda",
"Habash, Nizar"
] |
Investigating Gender Bias in {STEM} Job Advertisements
|
gebnlp-1.11
|
Poster
|
2306.07527v1
|
https://aclanthology.org/2024.gebnlp-1.12.bib
|
@inproceedings{stranisci-etal-2024-dissecting,
title = "Dissecting Biases in Relation Extraction: A Cross-Dataset Analysis on People{'}s Gender and Origin",
author = "Stranisci, Marco and
Huguet Cabot, Pere-Llu{\'\i}s and
Bassignana, Elisa and
Navigli, Roberto",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.12",
pages = "190--202",
abstract = "Relation Extraction (RE) is at the core of many Natural Language Understanding tasks, including knowledge-base population and Question Answering. However, any Natural Language Processing system is exposed to biases, and the analysis of these has not received much attention in RE. We propose a new method for inspecting bias in the RE pipeline, which is completely transparent in terms of interpretability. Specifically, in this work we analyze biases related to gender and place of birth. Our methodology includes (i) obtaining semantic triplets (subject, object, semantic relation) involving {`}person{'} entities from RE resources, (ii) collecting meta-information ({`}gender{'} and {`}place of birth{'}) using Entity Linking technologies, and then (iii) analyze the distribution of triplets across different groups (e.g., men versus women). We investigate bias at two levels: In the training data of three commonly used RE datasets (SREDFM, CrossRE, NYT), and in the predictions of a state-of-the-art RE approach (ReLiK). To enable cross-dataset analysis, we introduce a taxonomy of relation types mapping the label sets of different RE datasets to a unified label space. Our findings reveal that bias is a compounded issue affecting underrepresented groups within data and predictions for RE.",
}
|
Relation Extraction (RE) is at the core of many Natural Language Understanding tasks, including knowledge-base population and Question Answering. However, any Natural Language Processing system is exposed to biases, and the analysis of these has not received much attention in RE. We propose a new method for inspecting bias in the RE pipeline, which is completely transparent in terms of interpretability. Specifically, in this work we analyze biases related to gender and place of birth. Our methodology includes (i) obtaining semantic triplets (subject, object, semantic relation) involving {`}person{'} entities from RE resources, (ii) collecting meta-information ({`}gender{'} and {`}place of birth{'}) using Entity Linking technologies, and then (iii) analyze the distribution of triplets across different groups (e.g., men versus women). We investigate bias at two levels: In the training data of three commonly used RE datasets (SREDFM, CrossRE, NYT), and in the predictions of a state-of-the-art RE approach (ReLiK). To enable cross-dataset analysis, we introduce a taxonomy of relation types mapping the label sets of different RE datasets to a unified label space. Our findings reveal that bias is a compounded issue affecting underrepresented groups within data and predictions for RE.
|
[
"Stranisci, Marco",
"Huguet Cabot, Pere-Llu{\\'\\i}s",
"Bassignana, Elisa",
"Navigli, Roberto"
] |
Dissecting Biases in Relation Extraction: A Cross-Dataset Analysis on People{'}s Gender and Origin
|
gebnlp-1.12
|
Poster
|
2304.12810v1
|
https://aclanthology.org/2024.gebnlp-1.13.bib
|
@inproceedings{altinok-2024-gender,
title = "Gender Bias in {T}urkish Word Embeddings: A Comprehensive Study of Syntax, Semantics and Morphology Across Domains",
author = "Altinok, Duygu",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.13",
pages = "203--218",
abstract = "Gender bias in word representations has emerged as a prominent research area in recent years. While numerous studies have focused on measuring and addressing bias in English word embeddings, research on the Turkish language remains limited. This work aims to bridge this gap by conducting a comprehensive evaluation of gender bias in Turkish word embeddings, considering the dimensions of syntax, semantics, and morphology. We employ subword-based static word vectors trained on three distinct domains: web crawl, academical text, and medical text. Through the analysis of gender-associated words in each domain, we not only uncover gender bias but also gain insights into the unique characteristics of these domains. Additionally, we explore the influence of Turkish suffixes on word gender, providing a novel perspective on gender bias. Our findings reveal the pervasive nature of gender biases across various aspects of the Turkish language, including word frequency, semantics, parts-of-speech, and even the smallest linguistic unit - suffixes. Notably, we demonstrate that the majority of noun and verb lemmas, as well as adverbs and adjectives, exhibit masculine gendering in the general-purpose written language. This study is the first of its kind to offer a comprehensive examination of gender bias in the Turkish language.",
}
|
Gender bias in word representations has emerged as a prominent research area in recent years. While numerous studies have focused on measuring and addressing bias in English word embeddings, research on the Turkish language remains limited. This work aims to bridge this gap by conducting a comprehensive evaluation of gender bias in Turkish word embeddings, considering the dimensions of syntax, semantics, and morphology. We employ subword-based static word vectors trained on three distinct domains: web crawl, academical text, and medical text. Through the analysis of gender-associated words in each domain, we not only uncover gender bias but also gain insights into the unique characteristics of these domains. Additionally, we explore the influence of Turkish suffixes on word gender, providing a novel perspective on gender bias. Our findings reveal the pervasive nature of gender biases across various aspects of the Turkish language, including word frequency, semantics, parts-of-speech, and even the smallest linguistic unit - suffixes. Notably, we demonstrate that the majority of noun and verb lemmas, as well as adverbs and adjectives, exhibit masculine gendering in the general-purpose written language. This study is the first of its kind to offer a comprehensive examination of gender bias in the Turkish language.
|
[
"Altinok, Duygu"
] |
Gender Bias in {T}urkish Word Embeddings: A Comprehensive Study of Syntax, Semantics and Morphology Across Domains
|
gebnlp-1.13
|
Poster
|
2206.03390v1
|
https://aclanthology.org/2024.gebnlp-1.14.bib
|
@inproceedings{zhu-etal-2024-disagreeable,
title = "Disagreeable, Slovenly, Honest and Un-named Women? Investigating Gender Bias in {E}nglish Educational Resources by Extending Existing Gender Bias Taxonomies",
author = "Zhu, Haotian and
Gao, Kexin and
Xia, Fei and
Ostendorf, Mari",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.14",
pages = "219--236",
abstract = "Gender bias has been extensively studied in both the educational field and the Natural Language Processing (NLP) field, the former using human coding to identify patterns associated with and causes of gender bias in text and the latter to detect, measure and mitigate gender bias in NLP output and models. This work aims to use NLP to facilitate automatic, quantitative analysis of educational text within the framework of a gender bias taxonomy. Analyses of both educational texts and a lexical resource (WordNet) reveal patterns of bias that can inform and aid educators in updating textbooks and lexical resources and in designing assessment items.",
}
|
Gender bias has been extensively studied in both the educational field and the Natural Language Processing (NLP) field, the former using human coding to identify patterns associated with and causes of gender bias in text and the latter to detect, measure and mitigate gender bias in NLP output and models. This work aims to use NLP to facilitate automatic, quantitative analysis of educational text within the framework of a gender bias taxonomy. Analyses of both educational texts and a lexical resource (WordNet) reveal patterns of bias that can inform and aid educators in updating textbooks and lexical resources and in designing assessment items.
|
[
"Zhu, Haotian",
"Gao, Kexin",
"Xia, Fei",
"Ostendorf, Mari"
] |
Disagreeable, Slovenly, Honest and Un-named Women? Investigating Gender Bias in {E}nglish Educational Resources by Extending Existing Gender Bias Taxonomies
|
gebnlp-1.14
|
Poster
|
2209.03661v1
|
https://aclanthology.org/2024.gebnlp-1.15.bib
|
@inproceedings{garg-etal-2024-generating,
title = "Generating Gender Alternatives in Machine Translation",
author = "Garg, Sarthak and
Gheini, Mozhdeh and
Emmanuel, Clara and
Likhomanenko, Tatiana and
Gao, Qin and
Paulik, Matthias",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.15",
pages = "237--254",
abstract = "Machine translation (MT) systems often translate terms with ambiguous gender (e.g., English term {``}the nurse{''}) into the gendered form that is most prevalent in the systems{'} training data (e.g., {``}enfermera{''}, the Spanish term for a female nurse). This often reflects and perpetuates harmful stereotypes present in society. With MT user interfaces in mind that allow for resolving gender ambiguity in a frictionless manner, we study the problem of generating all grammatically correct gendered translation alternatives. We open source train and test datasets for five language pairs and establish benchmarks for this task. Our key technical contribution is a novel semi-supervised solution for generating alternatives that integrates seamlessly with standard MT models and maintains high performance without requiring additional components or increasing inference overhead.",
}
|
Machine translation (MT) systems often translate terms with ambiguous gender (e.g., English term {``}the nurse{''}) into the gendered form that is most prevalent in the systems{'} training data (e.g., {``}enfermera{''}, the Spanish term for a female nurse). This often reflects and perpetuates harmful stereotypes present in society. With MT user interfaces in mind that allow for resolving gender ambiguity in a frictionless manner, we study the problem of generating all grammatically correct gendered translation alternatives. We open source train and test datasets for five language pairs and establish benchmarks for this task. Our key technical contribution is a novel semi-supervised solution for generating alternatives that integrates seamlessly with standard MT models and maintains high performance without requiring additional components or increasing inference overhead.
|
[
"Garg, Sarthak",
"Gheini, Mozhdeh",
"Emmanuel, Clara",
"Likhomanenko, Tatiana",
"Gao, Qin",
"Paulik, Matthias"
] |
Generating Gender Alternatives in Machine Translation
|
gebnlp-1.15
|
Poster
|
2010.05332v2
|
https://aclanthology.org/2024.gebnlp-1.16.bib
|
@inproceedings{you-etal-2024-beyond,
title = "Beyond Binary Gender Labels: Revealing Gender Bias in {LLM}s through Gender-Neutral Name Predictions",
author = "You, Zhiwen and
Lee, HaeJin and
Mishra, Shubhanshu and
Jeoung, Sullam and
Mishra, Apratim and
Kim, Jinseok and
Diesner, Jana",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.16",
pages = "255--268",
abstract = "Name-based gender prediction has traditionally categorized individuals as either female or male based on their names, using a binary classification system. That binary approach can be problematic in the cases of gender-neutral names that do not align with any one gender, among other reasons. Relying solely on binary gender categories without recognizing gender-neutral names can reduce the inclusiveness of gender prediction tasks. We introduce an additional gender category, i.e., {``}neutral{''}, to study and address potential gender biases in Large Language Models (LLMs). We evaluate the performance of several foundational and large language models in predicting gender based on first names only. Additionally, we investigate the impact of adding birth years to enhance the accuracy of gender prediction, accounting for shifting associations between names and genders over time. Our findings indicate that most LLMs identify male and female names with high accuracy (over 80{\%}) but struggle with gender-neutral names (under 40{\%}), and the accuracy of gender prediction is higher for English-based first names than non-English names. The experimental results show that incorporating the birth year does not improve the overall accuracy of gender prediction, especially for names with evolving gender associations. We recommend using caution when applying LLMs for gender identification in downstream tasks, particularly when dealing with non-binary gender labels.",
}
|
Name-based gender prediction has traditionally categorized individuals as either female or male based on their names, using a binary classification system. That binary approach can be problematic in the cases of gender-neutral names that do not align with any one gender, among other reasons. Relying solely on binary gender categories without recognizing gender-neutral names can reduce the inclusiveness of gender prediction tasks. We introduce an additional gender category, i.e., {``}neutral{''}, to study and address potential gender biases in Large Language Models (LLMs). We evaluate the performance of several foundational and large language models in predicting gender based on first names only. Additionally, we investigate the impact of adding birth years to enhance the accuracy of gender prediction, accounting for shifting associations between names and genders over time. Our findings indicate that most LLMs identify male and female names with high accuracy (over 80{\%}) but struggle with gender-neutral names (under 40{\%}), and the accuracy of gender prediction is higher for English-based first names than non-English names. The experimental results show that incorporating the birth year does not improve the overall accuracy of gender prediction, especially for names with evolving gender associations. We recommend using caution when applying LLMs for gender identification in downstream tasks, particularly when dealing with non-binary gender labels.
|
[
"You, Zhiwen",
"Lee, HaeJin",
"Mishra, Shubhanshu",
"Jeoung, Sullam",
"Mishra, Apratim",
"Kim, Jinseok",
"Diesner, Jana"
] |
Beyond Binary Gender Labels: Revealing Gender Bias in {LLM}s through Gender-Neutral Name Predictions
|
gebnlp-1.16
|
Poster
|
2407.05271v1
|
https://aclanthology.org/2024.gebnlp-1.17.bib
|
@inproceedings{go-falenska-2024-gender,
title = "Is there Gender Bias in Dependency Parsing? Revisiting {``}Women{'}s Syntactic Resilience{''}",
author = "Go, Paul and
Falenska, Agnieszka",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.17",
pages = "269--279",
abstract = "In this paper, we revisit the seminal work of Garimella et al. 2019, who reported that dependency parsers learn demographically-related signals from their training data and perform differently on sentences authored by people of different genders. We re-run all the parsing experiments from Garimella et al. 2019 and find that their results are not reproducible. Additionally, the original patterns suggesting the presence of gender biases fail to generalize to other treebank and parsing architecture. Instead, our data analysis uncovers methodological shortcomings in the initial study that artificially introduced differences into female and male datasets during preprocessing. These disparities potentially compromised the validity of the original conclusions.",
}
|
In this paper, we revisit the seminal work of Garimella et al. 2019, who reported that dependency parsers learn demographically-related signals from their training data and perform differently on sentences authored by people of different genders. We re-run all the parsing experiments from Garimella et al. 2019 and find that their results are not reproducible. Additionally, the original patterns suggesting the presence of gender biases fail to generalize to other treebank and parsing architecture. Instead, our data analysis uncovers methodological shortcomings in the initial study that artificially introduced differences into female and male datasets during preprocessing. These disparities potentially compromised the validity of the original conclusions.
|
[
"Go, Paul",
"Falenska, Agnieszka"
] |
Is there Gender Bias in Dependency Parsing? Revisiting {``}Women{'}s Syntactic Resilience{''}
|
gebnlp-1.17
|
Poster
|
2404.01857v1
|
https://aclanthology.org/2024.gebnlp-1.18.bib
|
@inproceedings{bartl-leavy-2024-showgirls,
title = "From {`}Showgirls{'} to {`}Performers{'}: Fine-tuning with Gender-inclusive Language for Bias Reduction in {LLM}s",
author = "Bartl, Marion and
Leavy, Susan",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.18",
pages = "280--294",
abstract = "Gender bias is not only prevalent in Large Language Models (LLMs) and their training data, but also firmly ingrained into the structural aspects of language itself. Therefore, adapting linguistic structures within LLM training data to promote gender-inclusivity can make gender representations within the model more inclusive.The focus of our work are gender-exclusive affixes in English, such as in {`}show-girl{'} or {`}man-cave{'}, which can perpetuate gender stereotypes and binary conceptions of gender.We use an LLM training dataset to compile a catalogue of 692 gender-exclusive terms along with gender-neutral variants and from this, develop a gender-inclusive fine-tuning dataset, the {`}Tiny Heap{'}. Fine-tuning three different LLMs with this dataset, we observe an overall reduction in gender-stereotyping tendencies across the models. Our approach provides a practical method for enhancing gender inclusivity in LLM training data and contributes to incorporating queer-feminist linguistic activism in bias mitigation research in NLP.",
}
|
Gender bias is not only prevalent in Large Language Models (LLMs) and their training data, but also firmly ingrained into the structural aspects of language itself. Therefore, adapting linguistic structures within LLM training data to promote gender-inclusivity can make gender representations within the model more inclusive.The focus of our work are gender-exclusive affixes in English, such as in {`}show-girl{'} or {`}man-cave{'}, which can perpetuate gender stereotypes and binary conceptions of gender.We use an LLM training dataset to compile a catalogue of 692 gender-exclusive terms along with gender-neutral variants and from this, develop a gender-inclusive fine-tuning dataset, the {`}Tiny Heap{'}. Fine-tuning three different LLMs with this dataset, we observe an overall reduction in gender-stereotyping tendencies across the models. Our approach provides a practical method for enhancing gender inclusivity in LLM training data and contributes to incorporating queer-feminist linguistic activism in bias mitigation research in NLP.
|
[
"Bartl, Marion",
"Leavy, Susan"
] |
From {`}Showgirls{'} to {`}Performers{'}: Fine-tuning with Gender-inclusive Language for Bias Reduction in {LLM}s
|
gebnlp-1.18
|
Poster
|
2407.04434v1
|
https://aclanthology.org/2024.gebnlp-1.19.bib
|
@inproceedings{gupta-etal-2024-sociodemographic,
title = "Sociodemographic Bias in Language Models: A Survey and Forward Path",
author = "Gupta, Vipul and
Narayanan Venkit, Pranav and
Wilson, Shomir and
Passonneau, Rebecca",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.19",
pages = "295--322",
abstract = "Sociodemographic bias in language models (LMs) has the potential for harm when deployed in real-world settings. This paper presents a comprehensive survey of the past decade of research on sociodemographic bias in LMs, organized into a typology that facilitates examining the different aims: types of bias, quantifying bias, and debiasing techniques. We track the evolution of the latter two questions, then identify current trends and their limitations, as well as emerging techniques. To guide future research towards more effective and reliable solutions, and to help authors situate their work within this broad landscape, we conclude with a checklist of open questions.",
}
|
Sociodemographic bias in language models (LMs) has the potential for harm when deployed in real-world settings. This paper presents a comprehensive survey of the past decade of research on sociodemographic bias in LMs, organized into a typology that facilitates examining the different aims: types of bias, quantifying bias, and debiasing techniques. We track the evolution of the latter two questions, then identify current trends and their limitations, as well as emerging techniques. To guide future research towards more effective and reliable solutions, and to help authors situate their work within this broad landscape, we conclude with a checklist of open questions.
|
[
"Gupta, Vipul",
"Narayanan Venkit, Pranav",
"Wilson, Shomir",
"Passonneau, Rebecca"
] |
Sociodemographic Bias in Language Models: A Survey and Forward Path
|
gebnlp-1.19
|
Poster
|
2306.08158v4
|
https://aclanthology.org/2024.gebnlp-1.20.bib
|
@inproceedings{gautam-etal-2024-stop,
title = "Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in {NLP}",
author = "Gautam, Vagrant and
Subramonian, Arjun and
Lauscher, Anne and
Keyes, Os",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.20",
pages = "323--337",
abstract = "Personal names simultaneously differentiate individuals and categorize them in ways that are important in a given society. While the natural language processing community has thus associated personal names with sociodemographic characteristics in a variety of tasks, researchers have engaged to varying degrees with the established methodological problems in doing so. To guide future work that uses names and sociodemographic characteristics, we provide an overview of relevant research: first, we present an interdisciplinary background on names and naming. We then survey the issues inherent to associating names with sociodemographic attributes, covering problems of validity (e.g., systematic error, construct validity), as well as ethical concerns (e.g., harms, differential impact, cultural insensitivity). Finally, we provide guiding questions along with normative recommendations to avoid validity and ethical pitfalls when dealing with names and sociodemographic characteristics in natural language processing.",
}
|
Personal names simultaneously differentiate individuals and categorize them in ways that are important in a given society. While the natural language processing community has thus associated personal names with sociodemographic characteristics in a variety of tasks, researchers have engaged to varying degrees with the established methodological problems in doing so. To guide future work that uses names and sociodemographic characteristics, we provide an overview of relevant research: first, we present an interdisciplinary background on names and naming. We then survey the issues inherent to associating names with sociodemographic attributes, covering problems of validity (e.g., systematic error, construct validity), as well as ethical concerns (e.g., harms, differential impact, cultural insensitivity). Finally, we provide guiding questions along with normative recommendations to avoid validity and ethical pitfalls when dealing with names and sociodemographic characteristics in natural language processing.
|
[
"Gautam, Vagrant",
"Subramonian, Arjun",
"Lauscher, Anne",
"Keyes, Os"
] |
Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in {NLP}
|
gebnlp-1.20
|
Poster
|
0903.3797v1
|
https://aclanthology.org/2024.gebnlp-1.21.bib
|
@inproceedings{ghate-etal-2024-evaluating,
title = "Evaluating Gender Bias in Multilingual Multimodal {AI} Models: Insights from an {I}ndian Context",
author = "Ghate, Kshitish and
Choudhry, Arjun and
Bannihatti Kumar, Vanya",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.21",
pages = "338--350",
abstract = "We evaluate gender biases in multilingual multimodal image and text models in two settings: text-to-image retrieval and text-to-image generation, to show that even seemingly gender-neutral traits generate biased results. We evaluate our framework in the context of people from India, working with two languages: English and Hindi. We work with frameworks built around mCLIP-based models to ensure a thorough evaluation of recent state-of-the-art models in the multilingual setting due to their potential for widespread applications. We analyze the results across 50 traits for retrieval and 8 traits for generation, showing that current multilingual multimodal models are biased towards men for most traits, and this problem is further exacerbated for lower-resource languages like Hindi. We further discuss potential reasons behind this observation, particularly stemming from the bias introduced by the pretraining datasets.",
}
|
We evaluate gender biases in multilingual multimodal image and text models in two settings: text-to-image retrieval and text-to-image generation, to show that even seemingly gender-neutral traits generate biased results. We evaluate our framework in the context of people from India, working with two languages: English and Hindi. We work with frameworks built around mCLIP-based models to ensure a thorough evaluation of recent state-of-the-art models in the multilingual setting due to their potential for widespread applications. We analyze the results across 50 traits for retrieval and 8 traits for generation, showing that current multilingual multimodal models are biased towards men for most traits, and this problem is further exacerbated for lower-resource languages like Hindi. We further discuss potential reasons behind this observation, particularly stemming from the bias introduced by the pretraining datasets.
|
[
"Ghate, Kshitish",
"Choudhry, Arjun",
"Bannihatti Kumar, Vanya"
] |
Evaluating Gender Bias in Multilingual Multimodal {AI} Models: Insights from an {I}ndian Context
|
gebnlp-1.21
|
Poster
|
2307.01503v1
|
https://aclanthology.org/2024.gebnlp-1.22.bib
|
@inproceedings{bergstrand-gamback-2024-detecting,
title = "Detecting and Mitigating {LGBTQIA}+ Bias in Large {N}orwegian Language Models",
author = {Bergstrand, Selma and
Gamb{\"a}ck, Bj{\"o}rn},
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.22",
pages = "351--364",
abstract = "The paper aims to detect and mitigate LGBTQIA+ bias in large language models (LLMs). As the usage of LLMs quickly increases, so does the significance of the harms they may cause due to bias. The research field of bias in LLMs has seen massive growth, but few attempts have been made to detect or mitigate other biases than gender bias, and most focus has been on English LLMs. This work shows experimentally that LLMs may cause representational harms towards LGBTQIA+ individuals when evaluated on sentence completion tasks and on a benchmark dataset constructed from stereotypes reported by the queer community of Norway, collected through a survey in order to directly involve the affected community. Furthermore, Norwegian training corpora are probed for queer bias, revealing strong associations between queer terms and anti-queer slurs, as well as words related to pedophilia. Finally, a fine-tuning-based debiasing method is applied to two Norwegian LLMs. This method does not consistently reduce bias, but shows that queer bias can be altered, laying the foundation for future debiasing approaches. By shedding light on the severe discrimination that can occur through the usage of LLMs, this paper contributes to the ongoing fight for equal rights for the LGBTQIA+ community.",
}
|
The paper aims to detect and mitigate LGBTQIA+ bias in large language models (LLMs). As the usage of LLMs quickly increases, so does the significance of the harms they may cause due to bias. The research field of bias in LLMs has seen massive growth, but few attempts have been made to detect or mitigate other biases than gender bias, and most focus has been on English LLMs. This work shows experimentally that LLMs may cause representational harms towards LGBTQIA+ individuals when evaluated on sentence completion tasks and on a benchmark dataset constructed from stereotypes reported by the queer community of Norway, collected through a survey in order to directly involve the affected community. Furthermore, Norwegian training corpora are probed for queer bias, revealing strong associations between queer terms and anti-queer slurs, as well as words related to pedophilia. Finally, a fine-tuning-based debiasing method is applied to two Norwegian LLMs. This method does not consistently reduce bias, but shows that queer bias can be altered, laying the foundation for future debiasing approaches. By shedding light on the severe discrimination that can occur through the usage of LLMs, this paper contributes to the ongoing fight for equal rights for the LGBTQIA+ community.
|
[
"Bergstr",
", Selma",
"Gamb{\\\"a}ck, Bj{\\\"o}rn"
] |
Detecting and Mitigating {LGBTQIA}+ Bias in Large {N}orwegian Language Models
|
gebnlp-1.22
|
Poster
|
2207.10032v1
|
https://aclanthology.org/2024.gebnlp-1.23.bib
|
@inproceedings{stewart-mihalcea-2024-whose,
title = "Whose wife is it anyway? Assessing bias against same-gender relationships in machine translation",
author = "Stewart, Ian and
Mihalcea, Rada",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.23",
pages = "365--375",
abstract = "Machine translation often suffers from biased data and algorithms that can lead to unacceptable errors in system output. While bias in gender norms has been investigated, less is known about whether MT systems encode bias about social relationships, e.g., {``}the lawyer kissed her wife.{''} We investigate the degree of bias against same-gender relationships in MT systems, using generated template sentences drawn from several noun-gender languages (e.g., Spanish) and comprised of popular occupation nouns. We find that three popular MT services consistently fail to accurately translate sentences concerning relationships between entities of the same gender. The error rate varies considerably based on the context, and same-gender sentences referencing high female-representation occupations are translated with lower accuracy. We provide this work as a case study in the evaluation of intrinsic bias in NLP systems with respect to social relationships.",
}
|
Machine translation often suffers from biased data and algorithms that can lead to unacceptable errors in system output. While bias in gender norms has been investigated, less is known about whether MT systems encode bias about social relationships, e.g., {``}the lawyer kissed her wife.{''} We investigate the degree of bias against same-gender relationships in MT systems, using generated template sentences drawn from several noun-gender languages (e.g., Spanish) and comprised of popular occupation nouns. We find that three popular MT services consistently fail to accurately translate sentences concerning relationships between entities of the same gender. The error rate varies considerably based on the context, and same-gender sentences referencing high female-representation occupations are translated with lower accuracy. We provide this work as a case study in the evaluation of intrinsic bias in NLP systems with respect to social relationships.
|
[
"Stewart, Ian",
"Mihalcea, Rada"
] |
Whose wife is it anyway? Assessing bias against same-gender relationships in machine translation
|
gebnlp-1.23
|
Poster
|
2401.04972v2
|
https://aclanthology.org/2024.gebnlp-1.24.bib
|
@inproceedings{tahaei-bergler-2024-analysis,
title = "Analysis of Annotator Demographics in Sexism Detection",
author = "Tahaei, Narjes and
Bergler, Sabine",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.24",
pages = "376--383",
abstract = "This study explores the effect of annotators{'} demographic features on labeling sexist content in social media datasets, specifically focusing on the EXIST dataset, which includes direct sexist messages, reports and descriptions of sexist experiences and stereotypes. We investigate how various demographic backgrounds influence annotation outcomes and examine methods to incorporate these features into BERT-based model training. Our experiments demonstrate that adding demographic information improves performance in detecting sexism and assessing intention of the author.",
}
|
This study explores the effect of annotators{'} demographic features on labeling sexist content in social media datasets, specifically focusing on the EXIST dataset, which includes direct sexist messages, reports and descriptions of sexist experiences and stereotypes. We investigate how various demographic backgrounds influence annotation outcomes and examine methods to incorporate these features into BERT-based model training. Our experiments demonstrate that adding demographic information improves performance in detecting sexism and assessing intention of the author.
|
[
"Tahaei, Narjes",
"Bergler, Sabine"
] |
Analysis of Annotator Demographics in Sexism Detection
|
gebnlp-1.24
|
Poster
|
2108.03070v1
|
https://aclanthology.org/2024.gebnlp-1.25.bib
|
@inproceedings{sadhu-etal-2024-empirical,
title = "An Empirical Study of Gendered Stereotypes in Emotional Attributes for {B}angla in Multilingual Large Language Models",
author = "Sadhu, Jayanta and
Saha, Maneesha and
Shahriyar, Rifat",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.25",
pages = "384--398",
abstract = "The influence of Large Language Models (LLMs) is rapidly growing, automating more jobs over time. Assessing the fairness of LLMs is crucial due to their expanding impact. Studies reveal the reflection of societal norms and biases in LLMs, which creates a risk of propagating societal stereotypes in downstream tasks. Many studies on bias in LLMs focus on gender bias in various NLP applications. However, there{'}s a gap in research on bias in emotional attributes, despite the close societal link between emotion and gender. This gap is even larger for low-resource languages like Bangla. Historically, women are associated with emotions like empathy, fear, and guilt, while men are linked to anger, bravado, and authority. This pattern reflects societal norms in Bangla-speaking regions. We offer the first thorough investigation of gendered emotion attribution in Bangla for both closed and open source LLMs in this work. Our aim is to elucidate the intricate societal relationship between gender and emotion specifically within the context of Bangla. We have been successful in showing the existence of gender bias in the context of emotions in Bangla through analytical methods and also show how emotion attribution changes on the basis of gendered role selection in LLMs. All of our resources including code and data are made publicly available to support future research on Bangla NLP. Warning: This paper contains explicit stereotypical statements that many may find offensive.",
}
|
The influence of Large Language Models (LLMs) is rapidly growing, automating more jobs over time. Assessing the fairness of LLMs is crucial due to their expanding impact. Studies reveal the reflection of societal norms and biases in LLMs, which creates a risk of propagating societal stereotypes in downstream tasks. Many studies on bias in LLMs focus on gender bias in various NLP applications. However, there{'}s a gap in research on bias in emotional attributes, despite the close societal link between emotion and gender. This gap is even larger for low-resource languages like Bangla. Historically, women are associated with emotions like empathy, fear, and guilt, while men are linked to anger, bravado, and authority. This pattern reflects societal norms in Bangla-speaking regions. We offer the first thorough investigation of gendered emotion attribution in Bangla for both closed and open source LLMs in this work. Our aim is to elucidate the intricate societal relationship between gender and emotion specifically within the context of Bangla. We have been successful in showing the existence of gender bias in the context of emotions in Bangla through analytical methods and also show how emotion attribution changes on the basis of gendered role selection in LLMs. All of our resources including code and data are made publicly available to support future research on Bangla NLP. Warning: This paper contains explicit stereotypical statements that many may find offensive.
|
[
"Sadhu, Jayanta",
"Saha, Maneesha",
"Shahriyar, Rifat"
] |
An Empirical Study of Gendered Stereotypes in Emotional Attributes for {B}angla in Multilingual Large Language Models
|
gebnlp-1.25
|
Poster
|
2407.06432v1
|
https://aclanthology.org/2024.gebnlp-1.26.bib
|
@inproceedings{costa-jussa-etal-2024-overview,
title = "Overview of the Shared Task on Machine Translation Gender Bias Evaluation with Multilingual Holistic Bias",
author = "Costa-juss{\`a}, Marta and
Andrews, Pierre and
Basta, Christine and
Ciro, Juan and
Falenska, Agnieszka and
Goldfarb-Tarrant, Seraphina and
Mosquera, Rafael and
Nozza, Debora and
S{\'a}nchez, Eduardo",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Goldfarb-Tarrant, Seraphina and
Nozza, Debora",
booktitle = "Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.gebnlp-1.26",
pages = "399--404",
abstract = "We describe the details of the Shared Task of the 5th ACL Workshop on Gender Bias in Natural Language Processing (GeBNLP 2024). The task uses dataset to investigate the quality of Machine Translation systems on a particular case of gender robustness. We report baseline results as well as the results of the first participants. The shared task will be permanently available in the Dynabench platform.",
}
|
We describe the details of the Shared Task of the 5th ACL Workshop on Gender Bias in Natural Language Processing (GeBNLP 2024). The task uses dataset to investigate the quality of Machine Translation systems on a particular case of gender robustness. We report baseline results as well as the results of the first participants. The shared task will be permanently available in the Dynabench platform.
|
[
"Costa-juss{\\`a}, Marta",
"Andrews, Pierre",
"Basta, Christine",
"Ciro, Juan",
"Falenska, Agnieszka",
"Goldfarb-Tarrant, Seraphina",
"Mosquera, Rafael",
"Nozza, Debora",
"S{\\'a}nchez, Eduardo"
] |
Overview of the Shared Task on Machine Translation Gender Bias Evaluation with Multilingual Holistic Bias
|
gebnlp-1.26
|
Poster
|
2407.16266v1
|
https://aclanthology.org/2024.hucllm-1.1.bib
|
@inproceedings{chingacham-etal-2024-human,
title = "Human Speech Perception in Noise: Can Large Language Models Paraphrase to Improve It?",
author = "Chingacham, Anupama and
Zhang, Miaoran and
Demberg, Vera and
Klakow, Dietrich",
editor = "Soni, Nikita and
Flek, Lucie and
Sharma, Ashish and
Yang, Diyi and
Hooker, Sara and
Schwartz, H. Andrew",
booktitle = "Proceedings of the 1st Human-Centered Large Language Modeling Workshop",
month = aug,
year = "2024",
address = "TBD",
publisher = "ACL",
url = "https://aclanthology.org/2024.hucllm-1.1",
pages = "1--15",
abstract = "Large Language Models (LLMs) can generate text by transferring style attributes like formality resulting in formal or informal text.However, instructing LLMs to generate text that when spoken, is more intelligible in an acoustically difficult environment, is an under-explored topic.We conduct the first study to evaluate LLMs on a novel task of generating acoustically intelligible paraphrases for better human speech perception in noise.Our experiments in English demonstrated that with standard prompting, LLMs struggle to control the non-textual attribute, i.e., acoustic intelligibility, while efficiently capturing the desired textual attributes like semantic equivalence. To remedy this issue, we propose a simple prompting approach, prompt-and-select, which generates paraphrases by decoupling the desired textual and non-textual attributes in the text generation pipeline.Our approach resulted in a 40{\%} relative improvement in human speech perception, by paraphrasing utterances that are highly distorted in a listening condition with babble noise at signal-to-noise ratio (SNR) -5 dB. This study reveals the limitation of LLMs in capturing non-textual attributes, and our proposed method showcases the potential of using LLMs for better human speech perception in noise.",
}
|
Large Language Models (LLMs) can generate text by transferring style attributes like formality resulting in formal or informal text.However, instructing LLMs to generate text that when spoken, is more intelligible in an acoustically difficult environment, is an under-explored topic.We conduct the first study to evaluate LLMs on a novel task of generating acoustically intelligible paraphrases for better human speech perception in noise.Our experiments in English demonstrated that with standard prompting, LLMs struggle to control the non-textual attribute, i.e., acoustic intelligibility, while efficiently capturing the desired textual attributes like semantic equivalence. To remedy this issue, we propose a simple prompting approach, prompt-and-select, which generates paraphrases by decoupling the desired textual and non-textual attributes in the text generation pipeline.Our approach resulted in a 40{\%} relative improvement in human speech perception, by paraphrasing utterances that are highly distorted in a listening condition with babble noise at signal-to-noise ratio (SNR) -5 dB. This study reveals the limitation of LLMs in capturing non-textual attributes, and our proposed method showcases the potential of using LLMs for better human speech perception in noise.
|
[
"Chingacham, Anupama",
"Zhang, Miaoran",
"Demberg, Vera",
"Klakow, Dietrich"
] |
Human Speech Perception in Noise: Can Large Language Models Paraphrase to Improve It?
|
hucllm-1.1
|
Poster
|
2408.04029v1
|
https://aclanthology.org/2024.hucllm-1.2.bib
|
@inproceedings{pan-etal-2024-human,
title = "Human-Centered Design Recommendations for {LLM}-as-a-judge",
author = "Pan, Qian and
Ashktorab, Zahra and
Desmond, Michael and
Santill{\'a}n Cooper, Mart{\'\i}n and
Johnson, James and
Nair, Rahul and
Daly, Elizabeth and
Geyer, Werner",
editor = "Soni, Nikita and
Flek, Lucie and
Sharma, Ashish and
Yang, Diyi and
Hooker, Sara and
Schwartz, H. Andrew",
booktitle = "Proceedings of the 1st Human-Centered Large Language Modeling Workshop",
month = aug,
year = "2024",
address = "TBD",
publisher = "ACL",
url = "https://aclanthology.org/2024.hucllm-1.2",
pages = "16--29",
abstract = "Traditional reference-based metrics, such as BLEU and ROUGE, are less effective for assessing outputs from Large Language Models (LLMs) that produce highly creative or superior-quality text, or in situations where reference outputs are unavailable. While human evaluation remains an option, it is costly and difficult to scale. Recent work using LLMs as evaluators (LLM-as-a-judge) is promising, but trust and reliability remain a significant concern. Integrating human input is crucial to ensure criteria used to evaluate are aligned with the human{'}s intent, and evaluations are robust and consistent. This paper presents a user study of a design exploration called EvaluLLM, that enables users to leverage LLMs as customizable judges, promoting human involvement to balance trust and cost-saving potential with caution. Through interviews with eight domain experts, we identified the need for assistance in developing effective evaluation criteria aligning the LLM-as-a-judge with practitioners{'} preferences and expectations. We offer findings and design recommendations to optimize human-assisted LLM-as-judge systems.",
}
|
Traditional reference-based metrics, such as BLEU and ROUGE, are less effective for assessing outputs from Large Language Models (LLMs) that produce highly creative or superior-quality text, or in situations where reference outputs are unavailable. While human evaluation remains an option, it is costly and difficult to scale. Recent work using LLMs as evaluators (LLM-as-a-judge) is promising, but trust and reliability remain a significant concern. Integrating human input is crucial to ensure criteria used to evaluate are aligned with the human{'}s intent, and evaluations are robust and consistent. This paper presents a user study of a design exploration called EvaluLLM, that enables users to leverage LLMs as customizable judges, promoting human involvement to balance trust and cost-saving potential with caution. Through interviews with eight domain experts, we identified the need for assistance in developing effective evaluation criteria aligning the LLM-as-a-judge with practitioners{'} preferences and expectations. We offer findings and design recommendations to optimize human-assisted LLM-as-judge systems.
|
[
"Pan, Qian",
"Ashktorab, Zahra",
"Desmond, Michael",
"Santill{\\'a}n Cooper, Mart{\\'\\i}n",
"Johnson, James",
"Nair, Rahul",
"Daly, Elizabeth",
"Geyer, Werner"
] |
Human-Centered Design Recommendations for {LLM}-as-a-judge
|
hucllm-1.2
|
Poster
|
2404.07108v2
|
https://aclanthology.org/2024.hucllm-1.3.bib
|
@inproceedings{niu-etal-2024-parameter,
title = "Parameter-Efficient Detoxification with Contrastive Decoding",
author = "Niu, Tong and
Xiong, Caiming and
Zhou, Yingbo and
Yavuz, Semih",
editor = "Soni, Nikita and
Flek, Lucie and
Sharma, Ashish and
Yang, Diyi and
Hooker, Sara and
Schwartz, H. Andrew",
booktitle = "Proceedings of the 1st Human-Centered Large Language Modeling Workshop",
month = aug,
year = "2024",
address = "TBD",
publisher = "ACL",
url = "https://aclanthology.org/2024.hucllm-1.3",
pages = "30--40",
abstract = "The field of natural language generation has witnessed significant advancements in recent years, including the development of controllable text generation techniques. However, controlling the attributes of the generated text remains a challenge, especially when aiming to avoid undesirable behavior such as toxicity. In this work, we introduce Detoxification Generator (DETOXIGEN), an inference-time algorithm that steers the generation away from unwanted styles. DETOXIGEN is an ensemble of a pre-trained language model (generator) and a detoxifier. The detoxifier is trained intentionally on the toxic data representative of the undesirable attribute, encouraging it to generate text in that style exclusively. During the actual generation, we use the trained detoxifier to produce undesirable tokens for the generator to contrast against at each decoding step. This approach directly informs the generator to avoid generating tokens that the detoxifier considers highly likely. We evaluate DETOXIGEN on the commonly used REALTOXICITYPROMPTS benchmark (Gehman et al., 2020) with various language models as generators. We find that it significantly outperforms previous approaches in detoxification metrics while not compromising on the generation quality. Moreover, the detoxifier is obtained by soft prompt-tuning using the same backbone language model as the generator. Hence, DETOXIGEN requires only a tiny amount of extra weights from the virtual tokens of the detoxifier to be loaded into GPU memory while decoding, making it a promising lightweight, practical, and parameter-efficient detoxification strategy.",
}
|
The field of natural language generation has witnessed significant advancements in recent years, including the development of controllable text generation techniques. However, controlling the attributes of the generated text remains a challenge, especially when aiming to avoid undesirable behavior such as toxicity. In this work, we introduce Detoxification Generator (DETOXIGEN), an inference-time algorithm that steers the generation away from unwanted styles. DETOXIGEN is an ensemble of a pre-trained language model (generator) and a detoxifier. The detoxifier is trained intentionally on the toxic data representative of the undesirable attribute, encouraging it to generate text in that style exclusively. During the actual generation, we use the trained detoxifier to produce undesirable tokens for the generator to contrast against at each decoding step. This approach directly informs the generator to avoid generating tokens that the detoxifier considers highly likely. We evaluate DETOXIGEN on the commonly used REALTOXICITYPROMPTS benchmark (Gehman et al., 2020) with various language models as generators. We find that it significantly outperforms previous approaches in detoxification metrics while not compromising on the generation quality. Moreover, the detoxifier is obtained by soft prompt-tuning using the same backbone language model as the generator. Hence, DETOXIGEN requires only a tiny amount of extra weights from the virtual tokens of the detoxifier to be loaded into GPU memory while decoding, making it a promising lightweight, practical, and parameter-efficient detoxification strategy.
|
[
"Niu, Tong",
"Xiong, Caiming",
"Zhou, Yingbo",
"Yavuz, Semih"
] |
Parameter-Efficient Detoxification with Contrastive Decoding
|
hucllm-1.3
|
Poster
|
2402.15202v2
|
https://aclanthology.org/2024.hucllm-1.4.bib
|
@inproceedings{basar-etal-2024-extent,
title = "To What Extent Are Large Language Models Capable of Generating Substantial Reflections for Motivational Interviewing Counseling Chatbots? A Human Evaluation",
author = "Basar, Erkan and
Hendrickx, Iris and
Krahmer, Emiel and
Bruijn, Gert-Jan and
Bosse, Tibor",
editor = "Soni, Nikita and
Flek, Lucie and
Sharma, Ashish and
Yang, Diyi and
Hooker, Sara and
Schwartz, H. Andrew",
booktitle = "Proceedings of the 1st Human-Centered Large Language Modeling Workshop",
month = aug,
year = "2024",
address = "TBD",
publisher = "ACL",
url = "https://aclanthology.org/2024.hucllm-1.4",
pages = "41--52",
abstract = "Motivational Interviewing is a counselling style that requires skillful usage of reflective listening and engaging in conversations about sensitive and personal subjects. In this paper, we investigate to what extent we can use generative large language models in motivational interviewing chatbots to generate precise and variable reflections on user responses. We conduct a two-step human evaluation where we first independently assess the generated reflections based on four criteria essential to health counseling; appropriateness, specificity, naturalness, and engagement. In the second step, we compare the overall quality of generated and human-authored reflections via a ranking evaluation. We use GPT-4, BLOOM, and FLAN-T5 models to generate motivational interviewing reflections, based on real conversational data collected via chatbots designed to provide support for smoking cessation and sexual health. We discover that GPT-4 can produce reflections of a quality comparable to human-authored reflections. Finally, we conclude that large language models have the potential to enhance and expand reflections in predetermined health counseling chatbots, but a comprehensive manual review is advised.",
}
|
Motivational Interviewing is a counselling style that requires skillful usage of reflective listening and engaging in conversations about sensitive and personal subjects. In this paper, we investigate to what extent we can use generative large language models in motivational interviewing chatbots to generate precise and variable reflections on user responses. We conduct a two-step human evaluation where we first independently assess the generated reflections based on four criteria essential to health counseling; appropriateness, specificity, naturalness, and engagement. In the second step, we compare the overall quality of generated and human-authored reflections via a ranking evaluation. We use GPT-4, BLOOM, and FLAN-T5 models to generate motivational interviewing reflections, based on real conversational data collected via chatbots designed to provide support for smoking cessation and sexual health. We discover that GPT-4 can produce reflections of a quality comparable to human-authored reflections. Finally, we conclude that large language models have the potential to enhance and expand reflections in predetermined health counseling chatbots, but a comprehensive manual review is advised.
|
[
"Basar, Erkan",
"Hendrickx, Iris",
"Krahmer, Emiel",
"Bruijn, Gert-Jan",
"Bosse, Tibor"
] |
To What Extent Are Large Language Models Capable of Generating Substantial Reflections for Motivational Interviewing Counseling Chatbots? A Human Evaluation
|
hucllm-1.4
|
Poster
|
2407.08095v1
|
https://aclanthology.org/2024.hucllm-1.5.bib
|
@inproceedings{karamolegkou-etal-2024-vision,
title = "Vision-Language Models under Cultural and Inclusive Considerations",
author = "Karamolegkou, Antonia and
Rust, Phillip and
Cui, Ruixiang and
Cao, Yong and
S{\o}gaard, Anders and
Hershcovich, Daniel",
editor = "Soni, Nikita and
Flek, Lucie and
Sharma, Ashish and
Yang, Diyi and
Hooker, Sara and
Schwartz, H. Andrew",
booktitle = "Proceedings of the 1st Human-Centered Large Language Modeling Workshop",
month = aug,
year = "2024",
address = "TBD",
publisher = "ACL",
url = "https://aclanthology.org/2024.hucllm-1.5",
pages = "53--66",
abstract = "Large Vision Language Models can be used to assist visually impaired individuals by describing images they capture in their daily lives. Current evaluation datasets may not reflect the diverse cultural user backgrounds nor the situational context of this use case. To address this problem, we create a survey to determine caption preferences and propose a culture-centric evaluation benchmark by filtering VizWiz, an existing dataset with images taken by people who are blind. We then evaluate different models and prompts, investigating their reliability as visual assistants. While the evaluation results for state-of-the-art models seem promising, we identified some weak spots such as hallucinations and problems with conventional evaluation metrics. Our survey, data, code, and model outputs will be publicly available.",
}
|
Large Vision Language Models can be used to assist visually impaired individuals by describing images they capture in their daily lives. Current evaluation datasets may not reflect the diverse cultural user backgrounds nor the situational context of this use case. To address this problem, we create a survey to determine caption preferences and propose a culture-centric evaluation benchmark by filtering VizWiz, an existing dataset with images taken by people who are blind. We then evaluate different models and prompts, investigating their reliability as visual assistants. While the evaluation results for state-of-the-art models seem promising, we identified some weak spots such as hallucinations and problems with conventional evaluation metrics. Our survey, data, code, and model outputs will be publicly available.
|
[
"Karamolegkou, Antonia",
"Rust, Phillip",
"Cui, Ruixiang",
"Cao, Yong",
"S{\\o}gaard, Anders",
"Hershcovich, Daniel"
] |
Vision-Language Models under Cultural and Inclusive Considerations
|
hucllm-1.5
|
Poster
|
2407.06177v1
|
https://aclanthology.org/2024.hucllm-1.6.bib
|
@inproceedings{wu-etal-2024-evaluating,
title = "Evaluating Large Language Models on Social Signal Sensitivity: An Appraisal Theory Approach",
author = "Wu, Zhen and
Dutt, Ritam and
Rose, Carolyn",
editor = "Soni, Nikita and
Flek, Lucie and
Sharma, Ashish and
Yang, Diyi and
Hooker, Sara and
Schwartz, H. Andrew",
booktitle = "Proceedings of the 1st Human-Centered Large Language Modeling Workshop",
month = aug,
year = "2024",
address = "TBD",
publisher = "ACL",
url = "https://aclanthology.org/2024.hucllm-1.6",
pages = "67--80",
abstract = "We present a framework to assess the sensitivity of Large Language Models (LLMs) to textually embedded social signals using an Appraisal Theory perspective. We report on an experiment that uses prompts encoding three dimensions of social signals: Affect, Judgment, and Appreciation. In response to the prompt, an LLM generates both an analysis (Insight) and a conversational Response, which are analyzed in terms of sensitivity to the signals. We quantitatively evaluate the output text through topical analysis of the Insight and predicted social intelligence scores of the Response in terms of empathy and emotional polarity. Key findings show that LLMs are more sensitive to positive signals. The personas impact Responses but not the Insight. We discuss how our framework can be extended to a broader set of social signals, personas, and scenarios to evaluate LLM behaviors under various conditions.",
}
|
We present a framework to assess the sensitivity of Large Language Models (LLMs) to textually embedded social signals using an Appraisal Theory perspective. We report on an experiment that uses prompts encoding three dimensions of social signals: Affect, Judgment, and Appreciation. In response to the prompt, an LLM generates both an analysis (Insight) and a conversational Response, which are analyzed in terms of sensitivity to the signals. We quantitatively evaluate the output text through topical analysis of the Insight and predicted social intelligence scores of the Response in terms of empathy and emotional polarity. Key findings show that LLMs are more sensitive to positive signals. The personas impact Responses but not the Insight. We discuss how our framework can be extended to a broader set of social signals, personas, and scenarios to evaluate LLM behaviors under various conditions.
|
[
"Wu, Zhen",
"Dutt, Ritam",
"Rose, Carolyn"
] |
Evaluating Large Language Models on Social Signal Sensitivity: An Appraisal Theory Approach
|
hucllm-1.6
|
Poster
|
2310.14389v1
|
https://aclanthology.org/2024.hucllm-1.7.bib
|
@inproceedings{french-etal-2024-aligning,
title = "Aligning to Adults Is Easy, Aligning to Children Is Hard: A Study of Linguistic Alignment in Dialogue Systems",
author = "French, Dorothea and
D{'}Mello, Sidney and
Wense, Katharina",
editor = "Soni, Nikita and
Flek, Lucie and
Sharma, Ashish and
Yang, Diyi and
Hooker, Sara and
Schwartz, H. Andrew",
booktitle = "Proceedings of the 1st Human-Centered Large Language Modeling Workshop",
month = aug,
year = "2024",
address = "TBD",
publisher = "ACL",
url = "https://aclanthology.org/2024.hucllm-1.7",
pages = "81--87",
abstract = "During conversations, people align to one another over time, by using similar words, concepts, and syntax. This helps form a shared understanding of the conversational content and is associated with increased engagement and satisfaction. It also affects conversation outcomes: e.g., when talking to language learners, an above normal level of linguistic alignment of parents or language teachers is correlated with faster language acquisition. These benefits make human-like alignment an important property of dialogue systems, which has often been overlooked by the NLP community. In order to fill this gap, we ask: (RQ1) Due to the importance for engagement and satisfaction, to what degree do state-of-the-art dialogue systems align to adult users? (RQ2) With a potential application to child language acquisition in mind, do systems, similar to parents, show high levels of alignment during conversations with children? Our experiments show that ChatGPT aligns to adults at roughly human levels, while Llama2 shows elevated alignment. However, when responding to a child, both systems{'} alignment is below human levels.",
}
|
During conversations, people align to one another over time, by using similar words, concepts, and syntax. This helps form a shared understanding of the conversational content and is associated with increased engagement and satisfaction. It also affects conversation outcomes: e.g., when talking to language learners, an above normal level of linguistic alignment of parents or language teachers is correlated with faster language acquisition. These benefits make human-like alignment an important property of dialogue systems, which has often been overlooked by the NLP community. In order to fill this gap, we ask: (RQ1) Due to the importance for engagement and satisfaction, to what degree do state-of-the-art dialogue systems align to adult users? (RQ2) With a potential application to child language acquisition in mind, do systems, similar to parents, show high levels of alignment during conversations with children? Our experiments show that ChatGPT aligns to adults at roughly human levels, while Llama2 shows elevated alignment. However, when responding to a child, both systems{'} alignment is below human levels.
|
[
"French, Dorothea",
"D{'}Mello, Sidney",
"Wense, Katharina"
] |
Aligning to Adults Is Easy, Aligning to Children Is Hard: A Study of Linguistic Alignment in Dialogue Systems
|
hucllm-1.7
|
Poster
|
2406.17926v1
|
https://aclanthology.org/2024.iwslt-1.1.bib
|
@inproceedings{ahmad-etal-2024-findings,
title = "{FINDINGS} {OF} {THE} {IWSLT} 2024 {EVALUATION} {CAMPAIGN}",
author = {Ahmad, Ibrahim Said and
Anastasopoulos, Antonios and
Bojar, Ond{\v{r}}ej and
Borg, Claudia and
Carpuat, Marine and
Cattoni, Roldano and
Cettolo, Mauro and
Chen, William and
Dong, Qianqian and
Federico, Marcello and
Haddow, Barry and
Javorsk{\'y}, D{\'a}vid and
Krubi{\'n}ski, Mateusz and
Kim Lam, Tsz and
Ma, Xutai and
Mathur, Prashant and
Matusov, Evgeny and
Maurya, Chandresh and
McCrae, John and
Murray, Kenton and
Nakamura, Satoshi and
Negri, Matteo and
Niehues, Jan and
Niu, Xing and
Ojha, Atul Kr. and
Ortega, John and
Papi, Sara and
Pol{\'a}k, Peter and
Posp{\'\i}{\v{s}}il, Adam and
Pecina, Pavel and
Salesky, Elizabeth and
Sethiya, Nivedita and
Sarkar, Balaram and
Shi, Jiatong and
Sikasote, Claytone and
Sperber, Matthias and
St{\"u}ker, Sebastian and
Sudoh, Katsuhito and
Thompson, Brian and
Waibel, Alex and
Watanabe, Shinji and
Wilken, Patrick and
Zem{\'a}nek, Petr and
Zevallos, Rodolfo},
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.1",
pages = "1--11",
abstract = "This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, dialect and low-resource speech translation, and Indic languages. The shared tasks attracted 17 teams whose submissions are documented in 27 system papers. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.",
}
|
This paper reports on the shared tasks organized by the 21st IWSLT Conference. The shared tasks address 7 scientific challenges in spoken language translation: simultaneous and offline translation, automatic subtitling and dubbing, speech-to-speech translation, dialect and low-resource speech translation, and Indic languages. The shared tasks attracted 17 teams whose submissions are documented in 27 system papers. The growing interest towards spoken language translation is also witnessed by the constantly increasing number of shared task organizers and contributors to the overview paper, almost evenly distributed across industry and academia.
|
[
"Ahmad, Ibrahim Said",
"Anastasopoulos, Antonios",
"Bojar, Ond{\\v{r}}ej",
"Borg, Claudia",
"Carpuat, Marine",
"Cattoni, Roldano",
"Cettolo, Mauro",
"Chen, William",
"Dong, Qianqian",
"Federico, Marcello",
"Haddow, Barry",
"Javorsk{\\'y}, D{\\'a}vid",
"Krubi{\\'n}ski, Mateusz",
"Kim Lam, Tsz",
"Ma, Xutai",
"Mathur, Prashant",
"Matusov, Evgeny",
"Maurya, Ch",
"resh",
"McCrae, John",
"Murray, Kenton",
"Nakamura, Satoshi",
"Negri, Matteo",
"Niehues, Jan",
"Niu, Xing",
"Ojha, Atul Kr.",
"Ortega, John",
"Papi, Sara",
"Pol{\\'a}k, Peter",
"Posp{\\'\\i}{\\v{s}}il, Adam",
"Pecina, Pavel",
"Salesky, Elizabeth",
"Sethiya, Nivedita",
"Sarkar, Balaram",
"Shi, Jiatong",
"Sikasote, Claytone",
"Sperber, Matthias",
"St{\\\"u}ker, Sebastian",
"Sudoh, Katsuhito",
"Thompson, Brian",
"Waibel, Alex",
"Watanabe, Shinji",
"Wilken, Patrick",
"Zem{\\'a}nek, Petr",
"Zevallos, Rodolfo"
] |
{FINDINGS} {OF} {THE} {IWSLT} 2024 {EVALUATION} {CAMPAIGN}
|
iwslt-1.1
|
Poster
|
2407.00826v1
|
https://aclanthology.org/2024.iwslt-1.2.bib
|
@inproceedings{li-etal-2024-pause,
title = "Pause-Aware Automatic Dubbing using {LLM} and Voice Cloning",
author = "Li, Yuang and
Guo, Jiaxin and
Zhang, Min and
Miaomiao, Ma and
Rao, Zhiqiang and
Zhang, Weidong and
He, Xianghui and
Wei, Daimeng and
Yang, Hao",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.2",
pages = "12--16",
abstract = "Automatic dubbing aims to translate the speech of a video into another language, ensuring the new speech naturally fits the original video. This paper details Huawei Translation Services Center{'}s (HW-TSC) submission for IWSLT 2024{'}s automatic dubbing task, under an unconstrained setting. Our system{'}s machine translation (MT) component utilizes a Transformer-based MT model and an LLM-based post-editor to produce translations of varying lengths. The text-to-speech (TTS) component employs a VITS-based TTS model and a voice cloning module to emulate the original speaker{'}s vocal timbre. For enhanced dubbing synchrony, we introduce a parsing-informed pause selector. Finally, we rerank multiple results based on lip-sync error distance (LSE-D) and character error rate (CER). Our system achieves LSE-D of 10.75 and 12.19 on subset1 and subset2 of DE-EN test sets respectively, superior to last year{'}s best system.",
}
|
Automatic dubbing aims to translate the speech of a video into another language, ensuring the new speech naturally fits the original video. This paper details Huawei Translation Services Center{'}s (HW-TSC) submission for IWSLT 2024{'}s automatic dubbing task, under an unconstrained setting. Our system{'}s machine translation (MT) component utilizes a Transformer-based MT model and an LLM-based post-editor to produce translations of varying lengths. The text-to-speech (TTS) component employs a VITS-based TTS model and a voice cloning module to emulate the original speaker{'}s vocal timbre. For enhanced dubbing synchrony, we introduce a parsing-informed pause selector. Finally, we rerank multiple results based on lip-sync error distance (LSE-D) and character error rate (CER). Our system achieves LSE-D of 10.75 and 12.19 on subset1 and subset2 of DE-EN test sets respectively, superior to last year{'}s best system.
|
[
"Li, Yuang",
"Guo, Jiaxin",
"Zhang, Min",
"Miaomiao, Ma",
"Rao, Zhiqiang",
"Zhang, Weidong",
"He, Xianghui",
"Wei, Daimeng",
"Yang, Hao"
] |
Pause-Aware Automatic Dubbing using {LLM} and Voice Cloning
|
iwslt-1.2
|
Poster
|
2203.09708v1
|
https://aclanthology.org/2024.iwslt-1.3.bib
|
@inproceedings{dabre-song-2024-nicts,
title = "{NICT}{'}s Cascaded and End-To-End Speech Translation Systems using Whisper and {I}ndic{T}rans2 for the {I}ndic Task",
author = "Dabre, Raj and
Song, Haiyue",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.3",
pages = "17--22",
abstract = "This paper presents the NICT{'}s submission for the IWSLT 2024 Indic track, focusing on three speech-to-text (ST) translation directions: English to Hindi, Bengali, and Tamil. We aim to enhance translation quality in this low-resource scenario by integrating state-of-the-art pre-trained automated speech recognition (ASR) and text-to-text machine translation (MT) models. Our cascade system incorporates a Whisper model fine-tuned for ASR and an IndicTrans2 model fine-tuned for MT. Additionally, we propose an end-to-end system that combines a Whisper model for speech-to-text conversion with knowledge distilled from an IndicTrans2 MT model. We first fine-tune the IndicTrans2 model to generate pseudo data in Indic languages. This pseudo data, along with the original English speech data, is then used to fine-tune the Whisper model. Experimental results show that the cascaded system achieved a BLEU score of 51.0, outperforming the end-to-end model, which scored 19.1 BLEU. Moreover, the analysis indicates that applying knowledge distillation from the IndicTrans2 model to the end-to-end ST model improves the translation quality by about 0.7 BLEU.",
}
|
This paper presents the NICT{'}s submission for the IWSLT 2024 Indic track, focusing on three speech-to-text (ST) translation directions: English to Hindi, Bengali, and Tamil. We aim to enhance translation quality in this low-resource scenario by integrating state-of-the-art pre-trained automated speech recognition (ASR) and text-to-text machine translation (MT) models. Our cascade system incorporates a Whisper model fine-tuned for ASR and an IndicTrans2 model fine-tuned for MT. Additionally, we propose an end-to-end system that combines a Whisper model for speech-to-text conversion with knowledge distilled from an IndicTrans2 MT model. We first fine-tune the IndicTrans2 model to generate pseudo data in Indic languages. This pseudo data, along with the original English speech data, is then used to fine-tune the Whisper model. Experimental results show that the cascaded system achieved a BLEU score of 51.0, outperforming the end-to-end model, which scored 19.1 BLEU. Moreover, the analysis indicates that applying knowledge distillation from the IndicTrans2 model to the end-to-end ST model improves the translation quality by about 0.7 BLEU.
|
[
"Dabre, Raj",
"Song, Haiyue"
] |
{NICT}{'}s Cascaded and End-To-End Speech Translation Systems using Whisper and {I}ndic{T}rans2 for the {I}ndic Task
|
iwslt-1.3
|
Poster
|
2407.03809v1
|
https://aclanthology.org/2024.iwslt-1.4.bib
|
@inproceedings{palma-gomez-etal-2024-transforming,
title = "Transforming {LLM}s into Cross-modal and Cross-lingual Retrieval Systems",
author = "Palma Gomez, Frank and
Sanabria, Ramon and
Sung, Yun-hsuan and
Cer, Daniel and
Dalmia, Siddharth and
Hernandez Abrego, Gustavo",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.4",
pages = "23--32",
abstract = "Large language models (LLMs) are trained on text-only data that go far beyond the languages with paired speech and text data. At the same time, Dual Encoder (DE) based retrieval systems project queries and documents into the same embedding space and have demonstrated their success in retrieval and bi-text mining. To match speech and text in many languages, we propose using LLMs to initialize multi-modal DE retrieval systems. Unlike traditional methods, our system doesn{'}t require speech data during LLM pre-training and can exploit LLM{'}s multilingual text understanding capabilities to match speech and text in languages unseen during retrieval training. Our multi-modal LLM-based retrieval system is capable of matching speech and text in 102 languages despite only training on 21 languages. Our system outperforms previous systems trained explicitly on all 102 languages. We achieve a 10{\%} absolute improvement in Recall@1 averaged across these languages. Additionally, our model demonstrates cross-lingual speech and text matching, which is further enhanced by readily available machine translation data.",
}
|
Large language models (LLMs) are trained on text-only data that go far beyond the languages with paired speech and text data. At the same time, Dual Encoder (DE) based retrieval systems project queries and documents into the same embedding space and have demonstrated their success in retrieval and bi-text mining. To match speech and text in many languages, we propose using LLMs to initialize multi-modal DE retrieval systems. Unlike traditional methods, our system doesn{'}t require speech data during LLM pre-training and can exploit LLM{'}s multilingual text understanding capabilities to match speech and text in languages unseen during retrieval training. Our multi-modal LLM-based retrieval system is capable of matching speech and text in 102 languages despite only training on 21 languages. Our system outperforms previous systems trained explicitly on all 102 languages. We achieve a 10{\%} absolute improvement in Recall@1 averaged across these languages. Additionally, our model demonstrates cross-lingual speech and text matching, which is further enhanced by readily available machine translation data.
|
[
"Palma Gomez, Frank",
"Sanabria, Ramon",
"Sung, Yun-hsuan",
"Cer, Daniel",
"Dalmia, Siddharth",
"Hern",
"ez Abrego, Gustavo"
] |
Transforming {LLM}s into Cross-modal and Cross-lingual Retrieval Systems
|
iwslt-1.4
|
Poster
|
2403.00801v1
|
https://aclanthology.org/2024.iwslt-1.5.bib
|
@inproceedings{brazier-rouas-2024-conditioning,
title = "Conditioning {LLM}s with Emotion in Neural Machine Translation",
author = "Brazier, Charles and
Rouas, Jean-Luc",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.5",
pages = "33--38",
abstract = "Large Language Models (LLMs) have shown remarkable performance in Natural Language Processing tasks, including Machine Translation (MT). In this work, we propose a novel MT pipeline that integrates emotion information extracted from a Speech Emotion Recognition (SER) model into LLMs to enhance translation quality. We first fine-tune five existing LLMs on the Libri-trans dataset and select the most performant model. Subsequently, we augment LLM prompts with different dimensional emotions and train the selected LLM under these different configurations. Our experiments reveal that integrating emotion information, especially arousal, into LLM prompts leads to notable improvements in translation quality.",
}
|
Large Language Models (LLMs) have shown remarkable performance in Natural Language Processing tasks, including Machine Translation (MT). In this work, we propose a novel MT pipeline that integrates emotion information extracted from a Speech Emotion Recognition (SER) model into LLMs to enhance translation quality. We first fine-tune five existing LLMs on the Libri-trans dataset and select the most performant model. Subsequently, we augment LLM prompts with different dimensional emotions and train the selected LLM under these different configurations. Our experiments reveal that integrating emotion information, especially arousal, into LLM prompts leads to notable improvements in translation quality.
|
[
"Brazier, Charles",
"Rouas, Jean-Luc"
] |
Conditioning {LLM}s with Emotion in Neural Machine Translation
|
iwslt-1.5
|
Poster
|
2408.03150v1
|
https://aclanthology.org/2024.iwslt-1.6.bib
|
@inproceedings{zhang-etal-2024-nyas,
title = "The {NYA}{'}s Offline Speech Translation System for {IWSLT} 2024",
author = "Zhang, Yingxin and
Ma, Guodong and
Du, Binbin",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.6",
pages = "39--45",
abstract = "This paper reports the NYA{'}s submissions to IWSLT 2024 Offline Speech Translation (ST) task on the sub-tasks including English to Chinese, Japanese, and German. In detail, we participate in the unconstrained training track using the cascaded ST structure. For the automatic speech recognition (ASR) model, we use the Whisper large-v3 model. For the neural machine translation (NMT) model, the wider and deeper Transformer is adapted as the backbone model. Furthermore, we use data augmentation technologies to augment training data and data filtering strategies to improve the quality of training data. In addition, we explore many MT technologies such as Back Translation, Forward Translation, R-Drop, and Domain Adaptation.",
}
|
This paper reports the NYA{'}s submissions to IWSLT 2024 Offline Speech Translation (ST) task on the sub-tasks including English to Chinese, Japanese, and German. In detail, we participate in the unconstrained training track using the cascaded ST structure. For the automatic speech recognition (ASR) model, we use the Whisper large-v3 model. For the neural machine translation (NMT) model, the wider and deeper Transformer is adapted as the backbone model. Furthermore, we use data augmentation technologies to augment training data and data filtering strategies to improve the quality of training data. In addition, we explore many MT technologies such as Back Translation, Forward Translation, R-Drop, and Domain Adaptation.
|
[
"Zhang, Yingxin",
"Ma, Guodong",
"Du, Binbin"
] |
The {NYA}{'}s Offline Speech Translation System for {IWSLT} 2024
|
iwslt-1.6
|
Poster
|
2105.07319v2
|
https://aclanthology.org/2024.iwslt-1.7.bib
|
@inproceedings{wu-etal-2024-improving-quality,
title = "Improving the Quality of {IWLST} 2024 Cascade Offline Speech Translation and Speech-to-Speech Translation via Translation Hypothesis Ensembling with {NMT} models and Large Language Models",
author = "Wu, Zhanglin and
Guo, Jiaxin and
Wei, Daimeng and
Rao, Zhiqiang and
Li, Zongyao and
Shang, Hengchao and
Luo, Yuanchang and
Li, Shaojun and
Yang, Hao",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.7",
pages = "46--52",
abstract = "This paper presents HW-TSC{'}s submission to the IWSLT 2024 Offline Speech Translation Task and Speech-to-Speech Translation Task. The former includes three translation directions: English to German, English to Chinese, and English to Japanese, while the latter only includes the translation direction of English to Chinese. We attend all three tracks (Constraint training, Constrained with Large Language Models training, and Unconstrained training) of offline speech translation task, using the cascade model architecture. Under the constrained training track, we train an ASR model from scratch, and then employ R-Drop and domain data selection to train the NMT model. In the constrained with Large Language Models training track, we use Wav2vec 2.0 and mBART50 for ASR model training initialization, and then train the LLama2-7B-based MT model using continuous training with sentence-aligned parallel data, supervised fine-tuning, and contrastive preference optimization. In the unconstrained training track, we fine-tune the whisper model for speech recognition, and then ensemble the translation results of NMT models and LLMs to produce superior translation output. For the speech-to-speech translation Task, we initially employ the offline speech translation system described above to generate the translated text. Then, we utilize the VITS model to generate the corresponding speech and employ the OpenVoice model for timbre cloning.",
}
|
This paper presents HW-TSC{'}s submission to the IWSLT 2024 Offline Speech Translation Task and Speech-to-Speech Translation Task. The former includes three translation directions: English to German, English to Chinese, and English to Japanese, while the latter only includes the translation direction of English to Chinese. We attend all three tracks (Constraint training, Constrained with Large Language Models training, and Unconstrained training) of offline speech translation task, using the cascade model architecture. Under the constrained training track, we train an ASR model from scratch, and then employ R-Drop and domain data selection to train the NMT model. In the constrained with Large Language Models training track, we use Wav2vec 2.0 and mBART50 for ASR model training initialization, and then train the LLama2-7B-based MT model using continuous training with sentence-aligned parallel data, supervised fine-tuning, and contrastive preference optimization. In the unconstrained training track, we fine-tune the whisper model for speech recognition, and then ensemble the translation results of NMT models and LLMs to produce superior translation output. For the speech-to-speech translation Task, we initially employ the offline speech translation system described above to generate the translated text. Then, we utilize the VITS model to generate the corresponding speech and employ the OpenVoice model for timbre cloning.
|
[
"Wu, Zhanglin",
"Guo, Jiaxin",
"Wei, Daimeng",
"Rao, Zhiqiang",
"Li, Zongyao",
"Shang, Hengchao",
"Luo, Yuanchang",
"Li, Shaojun",
"Yang, Hao"
] |
Improving the Quality of {IWLST} 2024 Cascade Offline Speech Translation and Speech-to-Speech Translation via Translation Hypothesis Ensembling with {NMT} models and Large Language Models
|
iwslt-1.7
|
Poster
|
1607.01628v1
|
https://aclanthology.org/2024.iwslt-1.8.bib
|
@inproceedings{wei-etal-2024-hw,
title = "{HW}-{TSC}{'}s Speech to Text Translation System for {IWSLT} 2024 in {I}ndic track",
author = "Wei, Bin and
Li, Zongyao and
Guo, Jiaxin and
Wei, Daimeng and
Wu, Zhanglin and
Chen, Xiaoyu and
Rao, Zhiqiang and
Li, Shaojun and
Luo, Yuanchang and
Shang, Hengchao and
Yang, Hao and
Jiang, Yanfei",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.8",
pages = "53--56",
abstract = "This article introduces the process of HW-TSC and the results of IWSLT 2024 Indic Track Speech to Text Translation. We designed a cascade system consisting of an ASR model and a machine translation model to translate speech from one language to another. For the ASR part, we directly use whisper large v3 as our ASR model. Our main task is to optimize the machine translation model (en2ta, en2hi, en2bn). In the process of optimizing the translation model, we first use bilingual corpus to train the baseline model. Then we use monolingual data to construct pseudo-corpus data to further enhance the baseline model. Finally, we filter the parallel corpus data through the labse filtering method and finetune the model again, which can further improve the bleu value. We also selected domain data from bilingual corpus to finetune previous model to achieve the best results.",
}
|
This article introduces the process of HW-TSC and the results of IWSLT 2024 Indic Track Speech to Text Translation. We designed a cascade system consisting of an ASR model and a machine translation model to translate speech from one language to another. For the ASR part, we directly use whisper large v3 as our ASR model. Our main task is to optimize the machine translation model (en2ta, en2hi, en2bn). In the process of optimizing the translation model, we first use bilingual corpus to train the baseline model. Then we use monolingual data to construct pseudo-corpus data to further enhance the baseline model. Finally, we filter the parallel corpus data through the labse filtering method and finetune the model again, which can further improve the bleu value. We also selected domain data from bilingual corpus to finetune previous model to achieve the best results.
|
[
"Wei, Bin",
"Li, Zongyao",
"Guo, Jiaxin",
"Wei, Daimeng",
"Wu, Zhanglin",
"Chen, Xiaoyu",
"Rao, Zhiqiang",
"Li, Shaojun",
"Luo, Yuanchang",
"Shang, Hengchao",
"Yang, Hao",
"Jiang, Yanfei"
] |
{HW}-{TSC}{'}s Speech to Text Translation System for {IWSLT} 2024 in {I}ndic track
|
iwslt-1.8
|
Poster
|
2407.00826v1
|
https://aclanthology.org/2024.iwslt-1.9.bib
|
@inproceedings{gasan-pais-2024-multi,
title = "Multi-Model System for Effective Subtitling Compression",
author = "Gasan, Carol-Luca and
P{\u{a}}i{\textcommabelow{s}}, Vasile",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.9",
pages = "57--64",
abstract = "This paper presents RACAI{'}s system used for the shared task of {`}Subtitling track: Subtitle Compression{'} (the English to Spanish language direction), organized as part of {`}the 21st edition of The International Conference on Spoken Language Translation (IWSLT 2024){'}. The proposed system consists of multiple models whose outputs are then ensembled using an algorithm, which has the purpose of maximizing the similarity of the initial and resulting text. We present the introduced datasets and the models{'} training strategy, along with the reported results on the proposed test set.",
}
|
This paper presents RACAI{'}s system used for the shared task of {`}Subtitling track: Subtitle Compression{'} (the English to Spanish language direction), organized as part of {`}the 21st edition of The International Conference on Spoken Language Translation (IWSLT 2024){'}. The proposed system consists of multiple models whose outputs are then ensembled using an algorithm, which has the purpose of maximizing the similarity of the initial and resulting text. We present the introduced datasets and the models{'} training strategy, along with the reported results on the proposed test set.
|
[
"Gasan, Carol-Luca",
"P{\\u{a}}i{\\textcommabelow{s}}, Vasile"
] |
Multi-Model System for Effective Subtitling Compression
|
iwslt-1.9
|
Poster
|
2205.09360v1
|
https://aclanthology.org/2024.iwslt-1.10.bib
|
@inproceedings{savoldi-etal-2024-fbk,
title = "{FBK}@{IWSLT} Test Suites Task: Gender Bias evaluation with {M}u{ST}-{SHE}",
author = "Savoldi, Beatrice and
Gaido, Marco and
Negri, Matteo and
Bentivogli, Luisa",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.10",
pages = "65--71",
abstract = "This paper presents the FBK contribution to the IWSLT-2024 {`}Test suites{'} shared subtask, part of the Offline Speech Translation Task. Our contribution consists of the MuST-SHE-IWSLT24 benchmark evaluation, designed to assess gender bias in speech translation. By focusing on the en-de language pair, we rely on a newly created test suite to investigate systems{'} ability to correctly translate feminine and masculine gender. Our results indicate that {--} under realistic conditions {--} current ST systems achieve reasonable and comparable performance in correctly translating both feminine and masculine forms when contextual gender information is available. For ambiguous references to the speaker, however, we attest a consistent preference towards masculine gender, thus calling for future endeavours on the topic. Towards this goal we make MuST-SHE-IWSLT24 freely available at: https://mt.fbk.eu/must-she/",
}
|
This paper presents the FBK contribution to the IWSLT-2024 {`}Test suites{'} shared subtask, part of the Offline Speech Translation Task. Our contribution consists of the MuST-SHE-IWSLT24 benchmark evaluation, designed to assess gender bias in speech translation. By focusing on the en-de language pair, we rely on a newly created test suite to investigate systems{'} ability to correctly translate feminine and masculine gender. Our results indicate that {--} under realistic conditions {--} current ST systems achieve reasonable and comparable performance in correctly translating both feminine and masculine forms when contextual gender information is available. For ambiguous references to the speaker, however, we attest a consistent preference towards masculine gender, thus calling for future endeavours on the topic. Towards this goal we make MuST-SHE-IWSLT24 freely available at: https://mt.fbk.eu/must-she/
|
[
"Savoldi, Beatrice",
"Gaido, Marco",
"Negri, Matteo",
"Bentivogli, Luisa"
] |
{FBK}@{IWSLT} Test Suites Task: Gender Bias evaluation with {M}u{ST}-{SHE}
|
iwslt-1.10
|
Poster
|
2106.12607v2
|
https://aclanthology.org/2024.iwslt-1.11.bib
|
@inproceedings{papi-etal-2024-simulseamless,
title = "{S}imul{S}eamless: {FBK} at {IWSLT} 2024 Simultaneous Speech Translation",
author = "Papi, Sara and
Gaido, Marco and
Negri, Matteo and
Bentivogli, Luisa",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.11",
pages = "72--79",
abstract = "This paper describes the FBK{'}s participation in the Simultaneous Translation Evaluation Campaign at IWSLT 2024. For this year{'}s submission in the speech-to-text translation (ST) sub-track, we propose SimulSeamless, which is realized by combining AlignAtt and SeamlessM4T in its medium configuration. The SeamlessM4T model is used {`}off-the-shelf{'} and its simultaneous inference is enabled through the adoption of AlignAtt, a SimulST policy based on cross-attention that can be applied without any retraining or adaptation of the underlying model for the simultaneous task. We participated in all the Shared Task languages (English-{\textgreater}German, Japanese, Chinese, and Czech-{\textgreater}English), achieving acceptable or even better results compared to last year{'}s submissions. SimulSeamless, covering more than 143 source languages and 200 target languages, is released at: https://github.com/hlt-mt/FBK-fairseq/.",
}
|
This paper describes the FBK{'}s participation in the Simultaneous Translation Evaluation Campaign at IWSLT 2024. For this year{'}s submission in the speech-to-text translation (ST) sub-track, we propose SimulSeamless, which is realized by combining AlignAtt and SeamlessM4T in its medium configuration. The SeamlessM4T model is used {`}off-the-shelf{'} and its simultaneous inference is enabled through the adoption of AlignAtt, a SimulST policy based on cross-attention that can be applied without any retraining or adaptation of the underlying model for the simultaneous task. We participated in all the Shared Task languages (English-{\textgreater}German, Japanese, Chinese, and Czech-{\textgreater}English), achieving acceptable or even better results compared to last year{'}s submissions. SimulSeamless, covering more than 143 source languages and 200 target languages, is released at: https://github.com/hlt-mt/FBK-fairseq/.
|
[
"Papi, Sara",
"Gaido, Marco",
"Negri, Matteo",
"Bentivogli, Luisa"
] |
{S}imul{S}eamless: {FBK} at {IWSLT} 2024 Simultaneous Speech Translation
|
iwslt-1.11
|
Poster
|
2407.00826v1
|
https://aclanthology.org/2024.iwslt-1.12.bib
|
@inproceedings{zafar-etal-2024-setu,
title = "The {SETU}-{DCU} Submissions to {IWSLT} 2024 Low-Resource Speech-to-Text Translation Tasks",
author = "Zafar, Maria and
Castaldo, Antonio and
Nayak, Prashanth and
Haque, Rejwanul and
Gajakos, Neha and
Way, Andy",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.12",
pages = "80--85",
abstract = "Natural Language Processing (NLP) research and development has experienced rapid progression in the recent times due to advances in deep learning. The introduction of pre-trained large language models (LLMs) is at the core of this transformation, significantly enhancing the performance of machine translation (MT) and speech technologies. This development has also led to fundamental changes in modern translation and speech tools and their methodologies. However, there remain challenges when extending this progress to underrepresented dialects and low-resource languages, primarily due to the need for more data. This paper details our submissions to the IWSLT speech translation (ST) tasks. We used the Whisper model for the automatic speech recognition (ASR) component. We then used mBART and NLLB as cascaded systems for utilising their MT capabilities. Our research primarily focused on exploring various dialects of low-resource languages and harnessing existing resources from linguistically related languages. We conducted our experiments for two morphologically diverse language pairs: Irish-to-English and Maltese-to-English. We used BLEU, chrF and COMET for evaluating our MT models.",
}
|
Natural Language Processing (NLP) research and development has experienced rapid progression in the recent times due to advances in deep learning. The introduction of pre-trained large language models (LLMs) is at the core of this transformation, significantly enhancing the performance of machine translation (MT) and speech technologies. This development has also led to fundamental changes in modern translation and speech tools and their methodologies. However, there remain challenges when extending this progress to underrepresented dialects and low-resource languages, primarily due to the need for more data. This paper details our submissions to the IWSLT speech translation (ST) tasks. We used the Whisper model for the automatic speech recognition (ASR) component. We then used mBART and NLLB as cascaded systems for utilising their MT capabilities. Our research primarily focused on exploring various dialects of low-resource languages and harnessing existing resources from linguistically related languages. We conducted our experiments for two morphologically diverse language pairs: Irish-to-English and Maltese-to-English. We used BLEU, chrF and COMET for evaluating our MT models.
|
[
"Zafar, Maria",
"Castaldo, Antonio",
"Nayak, Prashanth",
"Haque, Rejwanul",
"Gajakos, Neha",
"Way, Andy"
] |
The {SETU}-{DCU} Submissions to {IWSLT} 2024 Low-Resource Speech-to-Text Translation Tasks
|
iwslt-1.12
|
Poster
|
2406.14177v1
|
https://aclanthology.org/2024.iwslt-1.13.bib
|
@inproceedings{gaido-etal-2024-automatic,
title = "Automatic Subtitling and Subtitle Compression: {FBK} at the {IWSLT} 2024 Subtitling track",
author = "Gaido, Marco and
Papi, Sara and
Cettolo, Mauro and
Cattoni, Roldano and
Piergentili, Andrea and
Negri, Matteo and
Bentivogli, Luisa",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.13",
pages = "86--96",
abstract = "The paper describes the FBK submissions to the Subtitling track of the 2024 IWSLT Evaluation Campaign, which covers both the Automatic Subtitling and the Subtitle Compression task for two language pairs: English to German (en-de) and English to Spanish (en-es). For the Automatic Subtitling task, we submitted two systems: i) a direct model, trained in constrained conditions, that produces the SRT files from the audio without intermediate outputs (e.g., transcripts), and ii) a cascade solution that integrates only free-to-use components, either taken off-the-shelf or developed in-house. Results show that, on both language pairs, our direct model outperforms both cascade and direct systems trained in constrained conditions in last year{'}s edition of the campaign, while our cascade solution is competitive with the best 2023 runs. For the Subtitle Compression task, our primary submission involved prompting a Large Language Model (LLM) in zero-shot mode to shorten subtitles that exceed the reading speed limit of 21 characters per second. Our results highlight the challenges inherent in shrinking out-of-context sentence fragments that are automatically generated and potentially error-prone, underscoring the need for future studies to develop targeted solutions.",
}
|
The paper describes the FBK submissions to the Subtitling track of the 2024 IWSLT Evaluation Campaign, which covers both the Automatic Subtitling and the Subtitle Compression task for two language pairs: English to German (en-de) and English to Spanish (en-es). For the Automatic Subtitling task, we submitted two systems: i) a direct model, trained in constrained conditions, that produces the SRT files from the audio without intermediate outputs (e.g., transcripts), and ii) a cascade solution that integrates only free-to-use components, either taken off-the-shelf or developed in-house. Results show that, on both language pairs, our direct model outperforms both cascade and direct systems trained in constrained conditions in last year{'}s edition of the campaign, while our cascade solution is competitive with the best 2023 runs. For the Subtitle Compression task, our primary submission involved prompting a Large Language Model (LLM) in zero-shot mode to shorten subtitles that exceed the reading speed limit of 21 characters per second. Our results highlight the challenges inherent in shrinking out-of-context sentence fragments that are automatically generated and potentially error-prone, underscoring the need for future studies to develop targeted solutions.
|
[
"Gaido, Marco",
"Papi, Sara",
"Cettolo, Mauro",
"Cattoni, Roldano",
"Piergentili, Andrea",
"Negri, Matteo",
"Bentivogli, Luisa"
] |
Automatic Subtitling and Subtitle Compression: {FBK} at the {IWSLT} 2024 Subtitling track
|
iwslt-1.13
|
Poster
|
2309.15554v1
|
https://aclanthology.org/2024.iwslt-1.14.bib
|
@inproceedings{nabhani-etal-2024-um,
title = "{UM} {IWSLT} 2024 Low-Resource Speech Translation: Combining {M}altese and {N}orth {L}evantine {A}rabic",
author = "Nabhani, Sara and
Williams, Aiden and
Jannat, Miftahul and
Rebecca Belcher, Kate and
Galea, Melanie and
Taylor, Anna and
Micallef, Kurt and
Borg, Claudia",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.14",
pages = "97--107",
abstract = "The IWSLT low-resource track encourages innovation in the field of speech translation, particularly in data-scarce conditions. This paper details our submission for the IWSLT 2024 low-resource track shared task for Maltese-English and North Levantine Arabic-English spoken language translation using an unconstrained pipeline approach. Using language models, we improve ASR performance by correcting the produced output. We present a 2 step approach for MT using data from external sources showing improvements over baseline systems. We also explore transliteration as a means to further augment MT data and exploit the cross-lingual similarities between Maltese and Arabic.",
}
|
The IWSLT low-resource track encourages innovation in the field of speech translation, particularly in data-scarce conditions. This paper details our submission for the IWSLT 2024 low-resource track shared task for Maltese-English and North Levantine Arabic-English spoken language translation using an unconstrained pipeline approach. Using language models, we improve ASR performance by correcting the produced output. We present a 2 step approach for MT using data from external sources showing improvements over baseline systems. We also explore transliteration as a means to further augment MT data and exploit the cross-lingual similarities between Maltese and Arabic.
|
[
"Nabhani, Sara",
"Williams, Aiden",
"Jannat, Miftahul",
"Rebecca Belcher, Kate",
"Galea, Melanie",
"Taylor, Anna",
"Micallef, Kurt",
"Borg, Claudia"
] |
{UM} {IWSLT} 2024 Low-Resource Speech Translation: Combining {M}altese and {N}orth {L}evantine {A}rabic
|
iwslt-1.14
|
Poster
|
1709.07276v1
|
https://aclanthology.org/2024.iwslt-1.15.bib
|
@inproceedings{abela-etal-2024-uom,
title = "{UOM}-Constrained {IWSLT} 2024 Shared Task Submission - {M}altese Speech Translation",
author = "Abela, Kurt and
Abdur Razzaq Riyadh, Md and
Galea, Melanie and
Busuttil, Alana and
Kovalev, Roman and
Williams, Aiden and
Borg, Claudia",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.15",
pages = "108--113",
abstract = "This paper presents our IWSLT-2024 shared task submission on the low-resource track. This submission forms part of the constrained setup; implying limited data for training. Following the introduction, this paper consists of a literature review defining previous approaches to speech translation, as well as their application to Maltese, followed by the defined methodology, evaluation and results, and the conclusion. A cascaded submission on the Maltese to English language pair is presented; consisting of a pipeline containing: a DeepSpeech 1 Automatic Speech Recognition (ASR) system, a KenLM model to optimise the transcriptions, and finally an LSTM machine translation model. The submission achieves a 0.5 BLEU score on the overall test set, and the ASR system achieves a word error rate of 97.15{\%}. Our code is made publicly available.",
}
|
This paper presents our IWSLT-2024 shared task submission on the low-resource track. This submission forms part of the constrained setup; implying limited data for training. Following the introduction, this paper consists of a literature review defining previous approaches to speech translation, as well as their application to Maltese, followed by the defined methodology, evaluation and results, and the conclusion. A cascaded submission on the Maltese to English language pair is presented; consisting of a pipeline containing: a DeepSpeech 1 Automatic Speech Recognition (ASR) system, a KenLM model to optimise the transcriptions, and finally an LSTM machine translation model. The submission achieves a 0.5 BLEU score on the overall test set, and the ASR system achieves a word error rate of 97.15{\%}. Our code is made publicly available.
|
[
"Abela, Kurt",
"Abdur Razzaq Riyadh, Md",
"Galea, Melanie",
"Busuttil, Alana",
"Kovalev, Roman",
"Williams, Aiden",
"Borg, Claudia"
] |
{UOM}-Constrained {IWSLT} 2024 Shared Task Submission - {M}altese Speech Translation
|
iwslt-1.15
|
Poster
|
2406.14177v1
|
https://aclanthology.org/2024.iwslt-1.16.bib
|
@inproceedings{kin-lam-etal-2024-compact,
title = "Compact Speech Translation Models via Discrete Speech Units Pretraining",
author = "Kin Lam, Tsz and
Birch, Alexandra and
Haddow, Barry",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.16",
pages = "114--124",
abstract = "We propose a pretraining method to use Self-Supervised Speech (SSS) model to creating more compact Speech-to-text Translation. In contrast to using the SSS model for initialization, our method is more suitable to memory constrained scenario such as on-device deployment. Our method is based on Discrete Speech Units (DSU) extracted from the SSS model. In the first step, our method pretrains two smaller encoder-decoder models on 1) Filterbank-to-DSU (Fbk-to-DSU) and 2) DSU-to-Translation (DSU-to-Trl) data respectively. The DSU thus become the distillation inputs of the smaller models. Subsequently, the encoder from the Fbk-to-DSU model and the decoder from the DSU-to-Trl model are taken to initialise the compact model. Finally, the compact model is finetuned on the paired Fbk-Trl data. In addition to being compact, our method requires no transcripts, making it applicable to low-resource settings. It also avoids speech discretization in inference and is more robust to the DSU tokenization. Evaluation on CoVoST-2 (X-En) shows that our method has consistent improvement over the baseline in three metrics while being compact i.e., only half the SSS model size.",
}
|
We propose a pretraining method to use Self-Supervised Speech (SSS) model to creating more compact Speech-to-text Translation. In contrast to using the SSS model for initialization, our method is more suitable to memory constrained scenario such as on-device deployment. Our method is based on Discrete Speech Units (DSU) extracted from the SSS model. In the first step, our method pretrains two smaller encoder-decoder models on 1) Filterbank-to-DSU (Fbk-to-DSU) and 2) DSU-to-Translation (DSU-to-Trl) data respectively. The DSU thus become the distillation inputs of the smaller models. Subsequently, the encoder from the Fbk-to-DSU model and the decoder from the DSU-to-Trl model are taken to initialise the compact model. Finally, the compact model is finetuned on the paired Fbk-Trl data. In addition to being compact, our method requires no transcripts, making it applicable to low-resource settings. It also avoids speech discretization in inference and is more robust to the DSU tokenization. Evaluation on CoVoST-2 (X-En) shows that our method has consistent improvement over the baseline in three metrics while being compact i.e., only half the SSS model size.
|
[
"Kin Lam, Tsz",
"Birch, Alex",
"ra",
"Haddow, Barry"
] |
Compact Speech Translation Models via Discrete Speech Units Pretraining
|
iwslt-1.16
|
Poster
|
2402.19333v2
|
https://aclanthology.org/2024.iwslt-1.17.bib
|
@inproceedings{e-ortega-etal-2024-quespa,
title = "{QUESPA} Submission for the {IWSLT} 2024 Dialectal and Low-resource Speech Translation Task",
author = "E. Ortega, John and
Joel Zevallos, Rodolfo and
Said Ahmad, Ibrahim and
Chen, William",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.17",
pages = "125--133",
abstract = "This article describes the QUESPA team speech translation (ST) submissions for the Quechua to Spanish (QUE{--}SPA) track featured in the Evaluation Campaign of IWSLT 2024: dialectal and low-resource speech translation. Two main submission types were supported in the campaign: constrained and unconstrained. This is our second year submitting our ST systems to the IWSLT shared task and we feel that we have achieved novel performance, surpassing last year{'}s submissions. Again, we were able to submit six total systems of which our best (primary) constrained system consisted of an ST model based on the Fairseq S2T framework where the audio representations were created using log mel-scale filter banks as features and the translations were performed using a transformer. The system was similar to last year{'}s submission with slight configuration changes, allowing us to achieve slightly higher performance (2 BLEU). Contrastingly, we were able to achieve much better performance than last year on the unconstrained task using a larger pre-trained language (PLM) model for ST (without cascading) and the inclusion of parallel QUE{--}SPA data found on the internet. The fine-tuning of Microsoft{'}s SpeechT5 model in a ST setting along with the addition of new data and a data augmentation technique allowed us to achieve 19.7 BLEU. Additionally, we present the other four submissions (2 constrained and 2 unconstrained) which are part of additional efforts of hyper-parameter and configuration tuning on existent models and the inclusion of Whisper for speech recognition",
}
|
This article describes the QUESPA team speech translation (ST) submissions for the Quechua to Spanish (QUE{--}SPA) track featured in the Evaluation Campaign of IWSLT 2024: dialectal and low-resource speech translation. Two main submission types were supported in the campaign: constrained and unconstrained. This is our second year submitting our ST systems to the IWSLT shared task and we feel that we have achieved novel performance, surpassing last year{'}s submissions. Again, we were able to submit six total systems of which our best (primary) constrained system consisted of an ST model based on the Fairseq S2T framework where the audio representations were created using log mel-scale filter banks as features and the translations were performed using a transformer. The system was similar to last year{'}s submission with slight configuration changes, allowing us to achieve slightly higher performance (2 BLEU). Contrastingly, we were able to achieve much better performance than last year on the unconstrained task using a larger pre-trained language (PLM) model for ST (without cascading) and the inclusion of parallel QUE{--}SPA data found on the internet. The fine-tuning of Microsoft{'}s SpeechT5 model in a ST setting along with the addition of new data and a data augmentation technique allowed us to achieve 19.7 BLEU. Additionally, we present the other four submissions (2 constrained and 2 unconstrained) which are part of additional efforts of hyper-parameter and configuration tuning on existent models and the inclusion of Whisper for speech recognition
|
[
"E. Ortega, John",
"Joel Zevallos, Rodolfo",
"Said Ahmad, Ibrahim",
"Chen, William"
] |
{QUESPA} Submission for the {IWSLT} 2024 Dialectal and Low-resource Speech Translation Task
|
iwslt-1.17
|
Poster
|
2205.01987v1
|
https://aclanthology.org/2024.iwslt-1.18.bib
|
@inproceedings{bamfo-odoom-etal-2024-speech,
title = "Speech Data from Radio Broadcasts for Low Resource Languages",
author = "Bamfo Odoom, Bismarck and
Paola Garcia Perera, Leibny and
Hansanti, Prangthip and
Barrault, Loic and
Ropers, Christophe and
Wiesner, Matthew and
Murray, Kenton and
Mourachko, Alexandre and
Koehn, Philipp",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.18",
pages = "134--139",
abstract = "We created a collection of speech data for 48 low resource languages. The corpus is extracted from radio broadcasts and processed with novel speech detection and language identification models based on a manually vetted subset of the audio for 10 languages. The data is made publicly available.",
}
|
We created a collection of speech data for 48 low resource languages. The corpus is extracted from radio broadcasts and processed with novel speech detection and language identification models based on a manually vetted subset of the audio for 10 languages. The data is made publicly available.
|
[
"Bamfo Odoom, Bismarck",
"Paola Garcia Perera, Leibny",
"Hansanti, Prangthip",
"Barrault, Loic",
"Ropers, Christophe",
"Wiesner, Matthew",
"Murray, Kenton",
"Mourachko, Alex",
"re",
"Koehn, Philipp"
] |
Speech Data from Radio Broadcasts for Low Resource Languages
|
iwslt-1.18
|
Poster
|
2306.00410v1
|
https://aclanthology.org/2024.iwslt-1.19.bib
|
@inproceedings{romney-robinson-etal-2024-jhu,
title = "{JHU} {IWSLT} 2024 Dialectal and Low-resource System Description",
author = "Romney Robinson, Nathaniel and
Sun, Kaiser and
Xiao, Cihan and
Bafna, Niyati and
Tan, Weiting and
Xu, Haoran and
Li Xinyuan, Henry and
Kejriwal, Ankur and
Khudanpur, Sanjeev and
Murray, Kenton and
McNamee, Paul",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.19",
pages = "140--153",
abstract = "Johns Hopkins University (JHU) submitted systems for all eight language pairs in the 2024 Low-Resource Language Track. The main effort of this work revolves around fine-tuning large and publicly available models in three proposed systems: i) end-to-end speech translation (ST) fine-tuning of Seamless4MT v2; ii) ST fine-tuning of Whisper; iii) a cascaded system involving automatic speech recognition with fine-tuned Whisper and machine translation with NLLB. On top of systems above, we conduct a comparative analysis on different training paradigms, such as intra-distillation for NLLB as well as joint training and curriculum learning for SeamlessM4T v2. Our results show that the best-performing approach differs by language pairs, but that i) fine-tuned SeamlessM4T v2 tends to perform best for source languages on which it was pre-trained, ii) multi-task training helps Whisper fine-tuning, iii) cascaded systems with Whisper and NLLB tend to outperform Whisper alone, and iv) intra-distillation helps NLLB fine-tuning.",
}
|
Johns Hopkins University (JHU) submitted systems for all eight language pairs in the 2024 Low-Resource Language Track. The main effort of this work revolves around fine-tuning large and publicly available models in three proposed systems: i) end-to-end speech translation (ST) fine-tuning of Seamless4MT v2; ii) ST fine-tuning of Whisper; iii) a cascaded system involving automatic speech recognition with fine-tuned Whisper and machine translation with NLLB. On top of systems above, we conduct a comparative analysis on different training paradigms, such as intra-distillation for NLLB as well as joint training and curriculum learning for SeamlessM4T v2. Our results show that the best-performing approach differs by language pairs, but that i) fine-tuned SeamlessM4T v2 tends to perform best for source languages on which it was pre-trained, ii) multi-task training helps Whisper fine-tuning, iii) cascaded systems with Whisper and NLLB tend to outperform Whisper alone, and iv) intra-distillation helps NLLB fine-tuning.
|
[
"Romney Robinson, Nathaniel",
"Sun, Kaiser",
"Xiao, Cihan",
"Bafna, Niyati",
"Tan, Weiting",
"Xu, Haoran",
"Li Xinyuan, Henry",
"Kejriwal, Ankur",
"Khudanpur, Sanjeev",
"Murray, Kenton",
"McNamee, Paul"
] |
{JHU} {IWSLT} 2024 Dialectal and Low-resource System Description
|
iwslt-1.19
|
Poster
|
2205.01987v1
|
https://aclanthology.org/2024.iwslt-1.20.bib
|
@inproceedings{xu-etal-2024-cmus,
title = "{CMU}{'}s {IWSLT} 2024 Simultaneous Speech Translation System",
author = "Xu, Xi and
Ouyang, Siqi and
Li, Lei",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.20",
pages = "154--159",
abstract = "This paper describes CMU{'}s submission to the IWSLT 2024 Simultaneous Speech Translation (SST) task for translating English speech to German text in a streaming manner. Our end-to-end speech-to-text (ST) system integrates the WavLM speech encoder, a modality adapter, and the Llama2-7B-Base model as the decoder. We employ a two-stage training approach: initially, we align the representations of speech and text, followed by full fine-tuning. Both stages are trained on MuST-c v2 data with cross-entropy loss. We adapt our offline ST model for SST using a simple fixed hold-n policy. Experiments show that our model obtains an offline BLEU score of 31.1 and a BLEU score of 29.5 under 2 seconds latency on the MuST-C-v2 tst-COMMON.",
}
|
This paper describes CMU{'}s submission to the IWSLT 2024 Simultaneous Speech Translation (SST) task for translating English speech to German text in a streaming manner. Our end-to-end speech-to-text (ST) system integrates the WavLM speech encoder, a modality adapter, and the Llama2-7B-Base model as the decoder. We employ a two-stage training approach: initially, we align the representations of speech and text, followed by full fine-tuning. Both stages are trained on MuST-c v2 data with cross-entropy loss. We adapt our offline ST model for SST using a simple fixed hold-n policy. Experiments show that our model obtains an offline BLEU score of 31.1 and a BLEU score of 29.5 under 2 seconds latency on the MuST-C-v2 tst-COMMON.
|
[
"Xu, Xi",
"Ouyang, Siqi",
"Li, Lei"
] |
{CMU}{'}s {IWSLT} 2024 Simultaneous Speech Translation System
|
iwslt-1.20
|
Poster
|
2407.00826v1
|
https://aclanthology.org/2024.iwslt-1.21.bib
|
@inproceedings{jiawei-etal-2024-hw,
title = "{HW}-{TSC}{'}s Submissions To the {IWSLT}2024 Low-resource Speech Translation Tasks",
author = "Jiawei, Zheng and
Shang, Hengchao and
Li, Zongyao and
Wu, Zhanglin and
Wei, Daimeng and
Rao, Zhiqiang and
Li, Shaojun and
Guo, Jiaxin and
Wei, Bin and
Luo, Yuanchang and
Yang, Hao",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.21",
pages = "160--163",
abstract = "In this work, we submitted our systems to the low-resource track of the IWSLT 2024 Speech Translation Campaign. Our systems tackled the unconstrained condition of the Dialectal Arabic North Levantine (ISO-3 code: apc) to English language pair. We proposed a cascaded solution consisting of an automatic speech recognition (ASR) model and a machine translation (MT) model. It was noted that the ASR model employed the pre-trained Whisper-large-v3 model to process the speech data, while the MT model adopted the Transformer architecture. To improve the quality of the MT model, it was stated that our system utilized not only the data provided by the competition but also an additional 54 million parallel sentences. Ultimately, we reported that our final system achieved a BLEU score of 24.7 for apc-to-English translation.",
}
|
In this work, we submitted our systems to the low-resource track of the IWSLT 2024 Speech Translation Campaign. Our systems tackled the unconstrained condition of the Dialectal Arabic North Levantine (ISO-3 code: apc) to English language pair. We proposed a cascaded solution consisting of an automatic speech recognition (ASR) model and a machine translation (MT) model. It was noted that the ASR model employed the pre-trained Whisper-large-v3 model to process the speech data, while the MT model adopted the Transformer architecture. To improve the quality of the MT model, it was stated that our system utilized not only the data provided by the competition but also an additional 54 million parallel sentences. Ultimately, we reported that our final system achieved a BLEU score of 24.7 for apc-to-English translation.
|
[
"Jiawei, Zheng",
"Shang, Hengchao",
"Li, Zongyao",
"Wu, Zhanglin",
"Wei, Daimeng",
"Rao, Zhiqiang",
"Li, Shaojun",
"Guo, Jiaxin",
"Wei, Bin",
"Luo, Yuanchang",
"Yang, Hao"
] |
{HW}-{TSC}{'}s Submissions To the {IWSLT}2024 Low-resource Speech Translation Tasks
|
iwslt-1.21
|
Poster
|
1809.01431v2
|
https://aclanthology.org/2024.iwslt-1.22.bib
|
@inproceedings{yan-etal-2024-cmus,
title = "{CMU}{'}s {IWSLT} 2024 Offline Speech Translation System: A Cascaded Approach For Long-Form Robustness",
author = "Yan, Brian and
Fernandes, Patrick and
Tian, Jinchuan and
Ouyang, Siqi and
Chen, William and
Livescu, Karen and
Li, Lei and
Neubig, Graham and
Watanabe, Shinji",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.22",
pages = "164--169",
abstract = "This work describes CMU{'}s submission to the IWSLT 2024 Offline Speech Translation (ST) Shared Task for translating English speech to German, Chinese, and Japanese text. We are the first participants to employ a long-form strategy which directly processes unsegmented recordings without the need for a separate voice-activity detection stage (VAD). We show that the Whisper automatic speech recognition (ASR) model has a hallucination problem when applied out-of-the-box to recordings containing non-speech noises, but a simple noisy fine-tuning approach can greatly enhance Whisper{'}s long-form robustness across multiple domains. Then, we feed English ASR outputs into fine-tuned NLLB machine translation (MT) models which are decoded using COMET-based Minimum Bayes Risk. Our VAD-free ASR+MT cascade is tested on TED talks, TV series, and workout videos and shown to outperform prior winning IWSLT submissions and large open-source models.",
}
|
This work describes CMU{'}s submission to the IWSLT 2024 Offline Speech Translation (ST) Shared Task for translating English speech to German, Chinese, and Japanese text. We are the first participants to employ a long-form strategy which directly processes unsegmented recordings without the need for a separate voice-activity detection stage (VAD). We show that the Whisper automatic speech recognition (ASR) model has a hallucination problem when applied out-of-the-box to recordings containing non-speech noises, but a simple noisy fine-tuning approach can greatly enhance Whisper{'}s long-form robustness across multiple domains. Then, we feed English ASR outputs into fine-tuned NLLB machine translation (MT) models which are decoded using COMET-based Minimum Bayes Risk. Our VAD-free ASR+MT cascade is tested on TED talks, TV series, and workout videos and shown to outperform prior winning IWSLT submissions and large open-source models.
|
[
"Yan, Brian",
"Fern",
"es, Patrick",
"Tian, Jinchuan",
"Ouyang, Siqi",
"Chen, William",
"Livescu, Karen",
"Li, Lei",
"Neubig, Graham",
"Watanabe, Shinji"
] |
{CMU}{'}s {IWSLT} 2024 Offline Speech Translation System: A Cascaded Approach For Long-Form Robustness
|
iwslt-1.22
|
Poster
|
2105.07319v2
|
https://aclanthology.org/2024.iwslt-1.23.bib
|
@inproceedings{ko-etal-2024-naist,
title = "{NAIST} Simultaneous Speech Translation System for {IWSLT} 2024",
author = "Ko, Yuka and
Fukuda, Ryo and
Nishikawa, Yuta and
Kano, Yasumasa and
Yanagita, Tomoya and
Doi, Kosuke and
Makinae, Mana and
Tan, Haotian and
Sakai, Makoto and
Sakti, Sakriani and
Sudoh, Katsuhito and
Nakamura, Satoshi",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.23",
pages = "170--182",
abstract = "This paper describes NAIST{'}s submission to the simultaneous track of the IWSLT 2024 Evaluation Campaign: English-to-German, Japanese, Chinese speech-to-text translation and English-to-Japanese speech-to-speech translation. We develop a multilingual end-to-end speech-to-text translation model combining two pre-trained language models, HuBERT and mBART. We trained this model with two decoding policies, Local Agreement (LA) and AlignAtt. The submitted models employ the LA policy because it outperformed the AlignAtt policy in previous models. Our speech-to-speech translation method is a cascade of the above speech-to-text model and an incremental text-to-speech (TTS) module that incorporates a phoneme estimation model, a parallel acoustic model, and a parallel WaveGAN vocoder. We improved our incremental TTS by applying the Transformer architecture with the AlignAtt policy for the estimation model. The results show that our upgraded TTS module contributed to improving the system performance.",
}
|
This paper describes NAIST{'}s submission to the simultaneous track of the IWSLT 2024 Evaluation Campaign: English-to-German, Japanese, Chinese speech-to-text translation and English-to-Japanese speech-to-speech translation. We develop a multilingual end-to-end speech-to-text translation model combining two pre-trained language models, HuBERT and mBART. We trained this model with two decoding policies, Local Agreement (LA) and AlignAtt. The submitted models employ the LA policy because it outperformed the AlignAtt policy in previous models. Our speech-to-speech translation method is a cascade of the above speech-to-text model and an incremental text-to-speech (TTS) module that incorporates a phoneme estimation model, a parallel acoustic model, and a parallel WaveGAN vocoder. We improved our incremental TTS by applying the Transformer architecture with the AlignAtt policy for the estimation model. The results show that our upgraded TTS module contributed to improving the system performance.
|
[
"Ko, Yuka",
"Fukuda, Ryo",
"Nishikawa, Yuta",
"Kano, Yasumasa",
"Yanagita, Tomoya",
"Doi, Kosuke",
"Makinae, Mana",
"Tan, Haotian",
"Sakai, Makoto",
"Sakti, Sakriani",
"Sudoh, Katsuhito",
"Nakamura, Satoshi"
] |
{NAIST} Simultaneous Speech Translation System for {IWSLT} 2024
|
iwslt-1.23
|
Poster
|
2407.00826v1
|
https://aclanthology.org/2024.iwslt-1.24.bib
|
@inproceedings{koneru-etal-2024-blending,
title = "Blending {LLM}s into Cascaded Speech Translation: {KIT}{'}s Offline Speech Translation System for {IWSLT} 2024",
author = "Koneru, Sai and
Binh Nguyen, Thai and
Pham, Ngoc-Quan and
Liu, Danni and
Li, Zhaolin and
Waibel, Alexander and
Niehues, Jan",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.24",
pages = "183--191",
abstract = "Large Language Models (LLMs) are currently under exploration for various tasks, including Automatic Speech Recognition (ASR), Machine Translation (MT), and even End-to-End Speech Translation (ST). In this paper, we present KIT{'}s offline submission in the constrained + LLM track by incorporating recently proposed techniques that can be added to any cascaded speech translation. Specifically, we integrate Mistral-7B into our system to enhance it in two ways. Firstly, we refine the ASR outputs by utilizing the N-best lists generated by our system and fine-tuning the LLM to predict the transcript accurately. Secondly, we refine the MT outputs at the document level by fine-tuning the LLM, leveraging both ASR and MT predictions to improve translation quality. We find that integrating the LLM into the ASR and MT systems results in an absolute improvement of 0.3{\%} in Word Error Rate and 0.65{\%} in COMET for tst2019 test set. In challenging test sets with overlapping speakers and background noise, we find that integrating LLM is not beneficial due to poor ASR performance. Here, we use ASR with chunked long-form decoding to improve context usage that may be unavailable when transcribing with Voice Activity Detection segmentation alone.",
}
|
Large Language Models (LLMs) are currently under exploration for various tasks, including Automatic Speech Recognition (ASR), Machine Translation (MT), and even End-to-End Speech Translation (ST). In this paper, we present KIT{'}s offline submission in the constrained + LLM track by incorporating recently proposed techniques that can be added to any cascaded speech translation. Specifically, we integrate Mistral-7B into our system to enhance it in two ways. Firstly, we refine the ASR outputs by utilizing the N-best lists generated by our system and fine-tuning the LLM to predict the transcript accurately. Secondly, we refine the MT outputs at the document level by fine-tuning the LLM, leveraging both ASR and MT predictions to improve translation quality. We find that integrating the LLM into the ASR and MT systems results in an absolute improvement of 0.3{\%} in Word Error Rate and 0.65{\%} in COMET for tst2019 test set. In challenging test sets with overlapping speakers and background noise, we find that integrating LLM is not beneficial due to poor ASR performance. Here, we use ASR with chunked long-form decoding to improve context usage that may be unavailable when transcribing with Voice Activity Detection segmentation alone.
|
[
"Koneru, Sai",
"Binh Nguyen, Thai",
"Pham, Ngoc-Quan",
"Liu, Danni",
"Li, Zhaolin",
"Waibel, Alex",
"er",
"Niehues, Jan"
] |
Blending {LLM}s into Cascaded Speech Translation: {KIT}{'}s Offline Speech Translation System for {IWSLT} 2024
|
iwslt-1.24
|
Poster
|
2406.16777v1
|
https://aclanthology.org/2024.iwslt-1.25.bib
|
@inproceedings{ben-kheder-etal-2024-aladan,
title = "{ALADAN} at {IWSLT}24 Low-resource {A}rabic Dialectal Speech Translation Task",
author = "Ben Kheder, Waad and
Jon, Josef and
Beyer, Andr{\'e} and
Messaoudi, Abdel and
Affan, Rabea and
Barras, Claude and
Tychonov, Maxim and
Gauvain, Jean-Luc",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.25",
pages = "192--202",
abstract = "This paper presents ALADAN{'}s approach to the IWSLT 2024 Dialectal and Low-resource shared task, focusing on Levantine Arabic (apc) and Tunisian Arabic (aeb) to English speech translation (ST). Addressing challenges such as the lack of standardized orthography and limited training data, we propose a solution for data normalization in Dialectal Arabic, employing a modified Levenshtein distance and Word2vec models to find orthographic variants of the same word. Our system consists of a cascade ST system integrating two ASR systems (TDNN-F and Zipformer) and two NMT modules derived from pre-trained models (NLLB-200 1.3B distilled model and CohereAI{'}s Command-R). Additionally, we explore the integration of unsupervised textual and audio data, highlighting the importance of multi-dialectal datasets for both ASR and NMT tasks. Our system achieves BLEU score of 31.5 for Levantine Arabic on the official validation set.",
}
|
This paper presents ALADAN{'}s approach to the IWSLT 2024 Dialectal and Low-resource shared task, focusing on Levantine Arabic (apc) and Tunisian Arabic (aeb) to English speech translation (ST). Addressing challenges such as the lack of standardized orthography and limited training data, we propose a solution for data normalization in Dialectal Arabic, employing a modified Levenshtein distance and Word2vec models to find orthographic variants of the same word. Our system consists of a cascade ST system integrating two ASR systems (TDNN-F and Zipformer) and two NMT modules derived from pre-trained models (NLLB-200 1.3B distilled model and CohereAI{'}s Command-R). Additionally, we explore the integration of unsupervised textual and audio data, highlighting the importance of multi-dialectal datasets for both ASR and NMT tasks. Our system achieves BLEU score of 31.5 for Levantine Arabic on the official validation set.
|
[
"Ben Kheder, Waad",
"Jon, Josef",
"Beyer, Andr{\\'e}",
"Messaoudi, Abdel",
"Affan, Rabea",
"Barras, Claude",
"Tychonov, Maxim",
"Gauvain, Jean-Luc"
] |
{ALADAN} at {IWSLT}24 Low-resource {A}rabic Dialectal Speech Translation Task
|
iwslt-1.25
|
Poster
|
2009.12622v1
|
https://aclanthology.org/2024.iwslt-1.26.bib
|
@inproceedings{kondo-etal-2024-enhancing,
title = "Enhancing Translation Accuracy of Large Language Models through Continual Pre-Training on Parallel Data",
author = "Kondo, Minato and
Utsuro, Takehito and
Nagata, Masaaki",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.26",
pages = "203--220",
abstract = "In this paper, we propose a two-phase training approach where pre-trained large language models are continually pre-trained on parallel data and then supervised fine-tuned with a small amount of high-quality parallel data. To investigate the effectiveness of our proposed approach, we conducted continual pre-training with a 3.8B-parameter model and parallel data across eight different formats. We evaluate these methods on thirteen test sets for Japanese-to-English and English-to-Japanese translation. The results demonstrate that when utilizing parallel data in continual pre-training, it is essential to alternate between source and target sentences. Additionally, we demonstrated that the translation accuracy improves only for translation directions where the order of source and target sentences aligns between continual pre-training data and inference. In addition, we demonstrate that the LLM-based translation model is more robust in translating spoken language and achieves higher accuracy with less training data compared to supervised encoder-decoder models. We also show that the highest accuracy is achieved when the data for continual pre-training consists of interleaved source and target sentences and when tags are added to the source sentences.",
}
|
In this paper, we propose a two-phase training approach where pre-trained large language models are continually pre-trained on parallel data and then supervised fine-tuned with a small amount of high-quality parallel data. To investigate the effectiveness of our proposed approach, we conducted continual pre-training with a 3.8B-parameter model and parallel data across eight different formats. We evaluate these methods on thirteen test sets for Japanese-to-English and English-to-Japanese translation. The results demonstrate that when utilizing parallel data in continual pre-training, it is essential to alternate between source and target sentences. Additionally, we demonstrated that the translation accuracy improves only for translation directions where the order of source and target sentences aligns between continual pre-training data and inference. In addition, we demonstrate that the LLM-based translation model is more robust in translating spoken language and achieves higher accuracy with less training data compared to supervised encoder-decoder models. We also show that the highest accuracy is achieved when the data for continual pre-training consists of interleaved source and target sentences and when tags are added to the source sentences.
|
[
"Kondo, Minato",
"Utsuro, Takehito",
"Nagata, Masaaki"
] |
Enhancing Translation Accuracy of Large Language Models through Continual Pre-Training on Parallel Data
|
iwslt-1.26
|
Poster
|
2407.03145v1
|
https://aclanthology.org/2024.iwslt-1.27.bib
|
@inproceedings{li-etal-2024-kit,
title = "The {KIT} Speech Translation Systems for {IWSLT} 2024 Dialectal and Low-resource Track",
author = "Li, Zhaolin and
Yavuz Ugan, Enes and
Liu, Danni and
Mullov, Carlos and
Anh Dinh, Tu and
Koneru, Sai and
Waibel, Alexander and
Niehues, Jan",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.27",
pages = "221--228",
abstract = "This paper presents KIT{'}s submissions to the IWSLT 2024 dialectal and low-resource track. In this work, we build systems for translating into English from speech in Maltese, Bemba, and two Arabic dialects Tunisian and North Levantine. Under the unconstrained condition, we leverage the pre-trained multilingual models by fine-tuning them for the target language pairs to address data scarcity problems in this track. We build cascaded and end-to-end speech translation systems for different language pairs and show the cascaded system brings slightly better overall performance. Besides, we find utilizing additional data resources boosts speech recognition performance but slightly harms machine translation performance in cascaded systems. Lastly, we show that Minimum Bayes Risk is effective in improving speech translation performance by combining the cascaded and end-to-end systems, bringing a consistent improvement of around 1 BLUE point.",
}
|
This paper presents KIT{'}s submissions to the IWSLT 2024 dialectal and low-resource track. In this work, we build systems for translating into English from speech in Maltese, Bemba, and two Arabic dialects Tunisian and North Levantine. Under the unconstrained condition, we leverage the pre-trained multilingual models by fine-tuning them for the target language pairs to address data scarcity problems in this track. We build cascaded and end-to-end speech translation systems for different language pairs and show the cascaded system brings slightly better overall performance. Besides, we find utilizing additional data resources boosts speech recognition performance but slightly harms machine translation performance in cascaded systems. Lastly, we show that Minimum Bayes Risk is effective in improving speech translation performance by combining the cascaded and end-to-end systems, bringing a consistent improvement of around 1 BLUE point.
|
[
"Li, Zhaolin",
"Yavuz Ugan, Enes",
"Liu, Danni",
"Mullov, Carlos",
"Anh Dinh, Tu",
"Koneru, Sai",
"Waibel, Alex",
"er",
"Niehues, Jan"
] |
The {KIT} Speech Translation Systems for {IWSLT} 2024 Dialectal and Low-resource Track
|
iwslt-1.27
|
Poster
|
2205.01987v1
|
https://aclanthology.org/2024.iwslt-1.28.bib
|
@inproceedings{singh-anand-etal-2024-empowering,
title = "Empowering Low-Resource Language Translation: Methodologies for {B}hojpuri-{H}indi and {M}arathi-{H}indi {ASR} and {MT}",
author = "Singh Anand, Harpreet and
Ratna Dash, Amulya and
Sharma, Yashvardhan",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.28",
pages = "229--234",
abstract = "The paper describes our submission for the unconstrained track of {`}Dialectal and Low-Resource Task{'} proposed in IWSLT-2024. We designed cascaded Speech Translation systems for the language pairs Marathi-Hindi and Bhojpuri-Hindi utilising and fine-tuning different pre-trained models for carrying out Automatic Speech Recognition (ASR) and Machine Translation (MT).",
}
|
The paper describes our submission for the unconstrained track of {`}Dialectal and Low-Resource Task{'} proposed in IWSLT-2024. We designed cascaded Speech Translation systems for the language pairs Marathi-Hindi and Bhojpuri-Hindi utilising and fine-tuning different pre-trained models for carrying out Automatic Speech Recognition (ASR) and Machine Translation (MT).
|
[
"Singh An",
", Harpreet",
"Ratna Dash, Amulya",
"Sharma, Yashvardhan"
] |
Empowering Low-Resource Language Translation: Methodologies for {B}hojpuri-{H}indi and {M}arathi-{H}indi {ASR} and {MT}
|
iwslt-1.28
|
Poster
|
1809.01431v2
|
https://aclanthology.org/2024.iwslt-1.29.bib
|
@inproceedings{liu-niehues-2024-recent,
title = "Recent Highlights in Multilingual and Multimodal Speech Translation",
author = "Liu, Danni and
Niehues, Jan",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.29",
pages = "235--253",
abstract = "Speech translation has witnessed significant progress driven by advancements in modeling techniques and the growing availability of training data. In this paper, we highlight recent advances in two ongoing research directions in ST: scaling the models to 1) many translation directions (multilingual ST) and 2) beyond the text output modality (multimodal ST). We structure this review by examining the sequential stages of a model{'}s development lifecycle: determining training resources, selecting model architecture, training procedures, evaluation metrics, and deployment considerations. We aim to highlight recent developments in each stage, with a particular focus on model architectures (dedicated speech translation models and LLM-based general-purpose model) and training procedures (task-specific vs. task-invariant approaches). Based on the reviewed advancements, we identify and discuss ongoing challenges within the field of speech translation.",
}
|
Speech translation has witnessed significant progress driven by advancements in modeling techniques and the growing availability of training data. In this paper, we highlight recent advances in two ongoing research directions in ST: scaling the models to 1) many translation directions (multilingual ST) and 2) beyond the text output modality (multimodal ST). We structure this review by examining the sequential stages of a model{'}s development lifecycle: determining training resources, selecting model architecture, training procedures, evaluation metrics, and deployment considerations. We aim to highlight recent developments in each stage, with a particular focus on model architectures (dedicated speech translation models and LLM-based general-purpose model) and training procedures (task-specific vs. task-invariant approaches). Based on the reviewed advancements, we identify and discuss ongoing challenges within the field of speech translation.
|
[
"Liu, Danni",
"Niehues, Jan"
] |
Recent Highlights in Multilingual and Multimodal Speech Translation
|
iwslt-1.29
|
Poster
|
2205.08180v1
|
https://aclanthology.org/2024.iwslt-1.30.bib
|
@inproceedings{doi-etal-2024-word,
title = "Word Order in {E}nglish-{J}apanese Simultaneous Interpretation: Analyses and Evaluation using Chunk-wise Monotonic Translation",
author = "Doi, Kosuke and
Ko, Yuka and
Makinae, Mana and
Sudoh, Katsuhito and
Nakamura, Satoshi",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.30",
pages = "254--264",
abstract = "This paper analyzes the features of monotonic translations, which follow the word order of the source language, in simultaneous interpreting (SI). Word order differences are one of the biggest challenges in SI, especially for language pairs with significant structural differences like English and Japanese. We analyzed the characteristics of chunk-wise monotonic translation (CMT) sentences using the NAIST English-to-Japanese Chunk-wise Monotonic Translation Evaluation Dataset and identified some grammatical structures that make monotonic translation difficult in English-Japanese SI. We further investigated the features of CMT sentences by evaluating the output from the existing speech translation (ST) and simultaneous speech translation (simulST) models on the NAIST English-to-Japanese Chunk-wise Monotonic Translation Evaluation Dataset as well as on existing test sets. The results indicate the possibility that the existing SI-based test set underestimates the model performance. The results also suggest that using CMT sentences as references gives higher scores to simulST models than ST models, and that using an offline-based test set to evaluate the simulST models underestimates the model performance.",
}
|
This paper analyzes the features of monotonic translations, which follow the word order of the source language, in simultaneous interpreting (SI). Word order differences are one of the biggest challenges in SI, especially for language pairs with significant structural differences like English and Japanese. We analyzed the characteristics of chunk-wise monotonic translation (CMT) sentences using the NAIST English-to-Japanese Chunk-wise Monotonic Translation Evaluation Dataset and identified some grammatical structures that make monotonic translation difficult in English-Japanese SI. We further investigated the features of CMT sentences by evaluating the output from the existing speech translation (ST) and simultaneous speech translation (simulST) models on the NAIST English-to-Japanese Chunk-wise Monotonic Translation Evaluation Dataset as well as on existing test sets. The results indicate the possibility that the existing SI-based test set underestimates the model performance. The results also suggest that using CMT sentences as references gives higher scores to simulST models than ST models, and that using an offline-based test set to evaluate the simulST models underestimates the model performance.
|
[
"Doi, Kosuke",
"Ko, Yuka",
"Makinae, Mana",
"Sudoh, Katsuhito",
"Nakamura, Satoshi"
] |
Word Order in {E}nglish-{J}apanese Simultaneous Interpretation: Analyses and Evaluation using Chunk-wise Monotonic Translation
|
iwslt-1.30
|
Poster
|
2406.08940v2
|
https://aclanthology.org/2024.iwslt-1.31.bib
|
@inproceedings{moslem-2024-leveraging,
title = "Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation",
author = "Moslem, Yasmin",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.31",
pages = "265--273",
abstract = "This paper describes our system submission to the International Conference on Spoken Language Translation (IWSLT 2024) for Irish-to-English speech translation. We built end-to-end systems based on Whisper, and employed a number of data augmentation techniques, such as speech back-translation and noise augmentation. We investigate the effect of using synthetic audio data and discuss several methods for enriching signal diversity.",
}
|
This paper describes our system submission to the International Conference on Spoken Language Translation (IWSLT 2024) for Irish-to-English speech translation. We built end-to-end systems based on Whisper, and employed a number of data augmentation techniques, such as speech back-translation and noise augmentation. We investigate the effect of using synthetic audio data and discuss several methods for enriching signal diversity.
|
[
"Moslem, Yasmin"
] |
Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation
|
iwslt-1.31
|
Poster
|
2406.17363v2
|
https://aclanthology.org/2024.iwslt-1.32.bib
|
@inproceedings{li-etal-2024-hw-tscs,
title = "{HW}-{TSC}{'}s Simultaneous Speech Translation System for {IWSLT} 2024",
author = "Li, Shaojun and
Rao, Zhiqiang and
Wei, Bin and
Luo, Yuanchang and
Wu, Zhanglin and
Li, Zongyao and
Shang, Hengchao and
Guo, Jiaxin and
Wei, Daimeng and
Yang, Hao",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.32",
pages = "274--279",
abstract = "This paper outlines our submission for the IWSLT 2024 Simultaneous Speech-to-Text (SimulS2T) and Speech-to-Speech (SimulS2S) Translation competition. We have engaged in all four language directions and both the SimulS2T and SimulS2S tracks: English-German (EN-DE), English-Chinese (EN-ZH), English-Japanese (EN-JA), and Czech-English (CS-EN). For the S2T track, we have built upon our previous year{'}s system and further honed the cascade system composed of ASR model and MT model. Concurrently, we have introduced an end-to-end system specifically for the CS-EN direction. This end-to-end (E2E) system primarily employs the pre-trained seamlessM4T model. In relation to the SimulS2S track, we have integrated a novel TTS model into our SimulS2T system. The final submission for the S2T directions of EN-DE, EN-ZH, and EN-JA has been refined over our championship system from last year. Building upon this foundation, the incorporation of the new TTS into our SimulS2S system has resulted in the ASR-BLEU surpassing last year{'}s best score.",
}
|
This paper outlines our submission for the IWSLT 2024 Simultaneous Speech-to-Text (SimulS2T) and Speech-to-Speech (SimulS2S) Translation competition. We have engaged in all four language directions and both the SimulS2T and SimulS2S tracks: English-German (EN-DE), English-Chinese (EN-ZH), English-Japanese (EN-JA), and Czech-English (CS-EN). For the S2T track, we have built upon our previous year{'}s system and further honed the cascade system composed of ASR model and MT model. Concurrently, we have introduced an end-to-end system specifically for the CS-EN direction. This end-to-end (E2E) system primarily employs the pre-trained seamlessM4T model. In relation to the SimulS2S track, we have integrated a novel TTS model into our SimulS2T system. The final submission for the S2T directions of EN-DE, EN-ZH, and EN-JA has been refined over our championship system from last year. Building upon this foundation, the incorporation of the new TTS into our SimulS2S system has resulted in the ASR-BLEU surpassing last year{'}s best score.
|
[
"Li, Shaojun",
"Rao, Zhiqiang",
"Wei, Bin",
"Luo, Yuanchang",
"Wu, Zhanglin",
"Li, Zongyao",
"Shang, Hengchao",
"Guo, Jiaxin",
"Wei, Daimeng",
"Yang, Hao"
] |
{HW}-{TSC}{'}s Simultaneous Speech Translation System for {IWSLT} 2024
|
iwslt-1.32
|
Poster
|
2407.00826v1
|
https://aclanthology.org/2024.iwslt-1.33.bib
|
@inproceedings{rishu-etal-2024-uom,
title = "{U}o{M}-{DFKI} submission to the low resource shared task",
author = "Rishu, Kumar and
Williams, Aiden and
Borg, Claudia and
Ostermann, Simon",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.33",
pages = "280--285",
abstract = "This system description paper presents the details of our primary and contrastive approaches to translating Maltese into English for IWSLT 24. The Maltese language shares a large vocabulary with Arabic and Italian languages, thus making it an ideal candidate to test the cross-lingual capabilities of recent state-of-the-art models. We experiment with two end-to-end approaches for our submissions: the Whisper and wav2vec 2.0 models. Our primary system gets a BLEU score of 35.1 on the combined data, whereas our contrastive approach gets 18.5. We also provide a manual analysis of our contrastive approach to identify some pitfalls that may have caused this difference.",
}
|
This system description paper presents the details of our primary and contrastive approaches to translating Maltese into English for IWSLT 24. The Maltese language shares a large vocabulary with Arabic and Italian languages, thus making it an ideal candidate to test the cross-lingual capabilities of recent state-of-the-art models. We experiment with two end-to-end approaches for our submissions: the Whisper and wav2vec 2.0 models. Our primary system gets a BLEU score of 35.1 on the combined data, whereas our contrastive approach gets 18.5. We also provide a manual analysis of our contrastive approach to identify some pitfalls that may have caused this difference.
|
[
"Rishu, Kumar",
"Williams, Aiden",
"Borg, Claudia",
"Ostermann, Simon"
] |
{U}o{M}-{DFKI} submission to the low resource shared task
|
iwslt-1.33
|
Poster
|
1810.07125v3
|
https://aclanthology.org/2024.iwslt-1.34.bib
|
@inproceedings{xie-etal-2024-hw,
title = "{HW}-{TSC}{'}s submission to the {IWSLT} 2024 Subtitling track",
author = "Xie, Yuhao and
Luo, Yuanchang and
Li, Zongyao and
Wu, Zhanglin and
Chen, Xiaoyu and
Rao, Zhiqiang and
Li, Shaojun and
Shang, Hengchao and
Guo, Jiaxin and
Wei, Daimeng and
Yang, Hao",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.34",
pages = "286--290",
abstract = "This paper introduces HW-TSC{'}s submission to the IWSLT 2024 Subtitling track. For the automatic subtitling track, we use an unconstrained cascaded strategy, with the main steps being: ASR with word-level timestamps, sentence segmentation based on punctuation restoration, further alignment using CTC or using machine translation with length penalty. For the subtitle compression track, we employ a subtitle compression strategy that integrates machine translation models and extensive rewriting models. We acquire the subtitle text requiring revision through the CPS index, then utilize a translation model to obtain the English version of this text. Following this, we extract the compressed-length subtitle text through controlled decoding. If this method fails to compress the text successfully, we resort to the Llama2 few-shot model for further compression.",
}
|
This paper introduces HW-TSC{'}s submission to the IWSLT 2024 Subtitling track. For the automatic subtitling track, we use an unconstrained cascaded strategy, with the main steps being: ASR with word-level timestamps, sentence segmentation based on punctuation restoration, further alignment using CTC or using machine translation with length penalty. For the subtitle compression track, we employ a subtitle compression strategy that integrates machine translation models and extensive rewriting models. We acquire the subtitle text requiring revision through the CPS index, then utilize a translation model to obtain the English version of this text. Following this, we extract the compressed-length subtitle text through controlled decoding. If this method fails to compress the text successfully, we resort to the Llama2 few-shot model for further compression.
|
[
"Xie, Yuhao",
"Luo, Yuanchang",
"Li, Zongyao",
"Wu, Zhanglin",
"Chen, Xiaoyu",
"Rao, Zhiqiang",
"Li, Shaojun",
"Shang, Hengchao",
"Guo, Jiaxin",
"Wei, Daimeng",
"Yang, Hao"
] |
{HW}-{TSC}{'}s submission to the {IWSLT} 2024 Subtitling track
|
iwslt-1.34
|
Poster
|
2309.15554v1
|
https://aclanthology.org/2024.iwslt-1.35.bib
|
@inproceedings{awiszus-etal-2024-charles,
title = "{C}harles Locock, Lowcock or Lockhart? Offline Speech Translation: Test Suite for Named Entities",
author = {Awiszus, Maximilian and
Niehues, Jan and
Turchi, Marco and
St{\"u}ker, Sebastian and
Waibel, Alex},
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.35",
pages = "291--297",
abstract = "Generating rare words is a challenging task for natural language processing in general and in speech translation (ST) specifically. This paper introduces a test suite prepared for the Offline ST shared task at IWSLT. In the test suite, corresponding rare words (i.e. named entities) were annotated on TED-Talks for English and German and the English side was made available to the participants together with some distractors (irrelevant named entities). Our evaluation checks the capabilities of ST systems to leverage the information in the contextual list of named entities and improve translation quality. Systems are ranked based on the recall and precision of named entities (separately on person, location, and organization names) in the translated texts. Our evaluation shows that using contextual information improves translation quality as well as the recall and precision of NEs. The recall of organization names in all submissions is the lowest of all categories with a maximum of 87.5 {\%} confirming the difficulties of ST systems in dealing with names.",
}
|
Generating rare words is a challenging task for natural language processing in general and in speech translation (ST) specifically. This paper introduces a test suite prepared for the Offline ST shared task at IWSLT. In the test suite, corresponding rare words (i.e. named entities) were annotated on TED-Talks for English and German and the English side was made available to the participants together with some distractors (irrelevant named entities). Our evaluation checks the capabilities of ST systems to leverage the information in the contextual list of named entities and improve translation quality. Systems are ranked based on the recall and precision of named entities (separately on person, location, and organization names) in the translated texts. Our evaluation shows that using contextual information improves translation quality as well as the recall and precision of NEs. The recall of organization names in all submissions is the lowest of all categories with a maximum of 87.5 {\%} confirming the difficulties of ST systems in dealing with names.
|
[
"Awiszus, Maximilian",
"Niehues, Jan",
"Turchi, Marco",
"St{\\\"u}ker, Sebastian",
"Waibel, Alex"
] |
{C}harles Locock, Lowcock or Lockhart? Offline Speech Translation: Test Suite for Named Entities
|
iwslt-1.35
|
Poster
|
2302.10186v7
|
https://aclanthology.org/2024.iwslt-1.36.bib
|
@inproceedings{issam-etal-2024-fixed,
title = "Fixed and Adaptive Simultaneous Machine Translation Strategies Using Adapters",
author = "Issam, Abderrahmane and
Can Semerci, Yusuf and
Scholtes, Jan and
Spanakis, Gerasimos",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.36",
pages = "298--310",
abstract = "Simultaneous machine translation aims at solving the task of real-time translation by starting to translate before consuming the full input, which poses challenges in terms of balancing quality and latency of the translation. The wait-k policy offers a solution by starting to translate after consuming words, where the choice of the number k directly affects the latency and quality. In applications where we seek to keep the choice over latency and quality at inference, the wait-k policy obliges us to train more than one model. In this paper, we address the challenge of building one model that can fulfil multiple latency levels and we achieve this by introducing lightweight adapter modules into the decoder. The adapters are trained to be specialized for different wait-k values and compared to other techniques they offer more flexibility to allow for reaping the benefits of parameter sharing and minimizing interference. Additionally, we show that by combining with an adaptive strategy, we can further improve the results. Experiments on two language directions show that our method outperforms or competes with other strong baselines on most latency values.",
}
|
Simultaneous machine translation aims at solving the task of real-time translation by starting to translate before consuming the full input, which poses challenges in terms of balancing quality and latency of the translation. The wait-k policy offers a solution by starting to translate after consuming words, where the choice of the number k directly affects the latency and quality. In applications where we seek to keep the choice over latency and quality at inference, the wait-k policy obliges us to train more than one model. In this paper, we address the challenge of building one model that can fulfil multiple latency levels and we achieve this by introducing lightweight adapter modules into the decoder. The adapters are trained to be specialized for different wait-k values and compared to other techniques they offer more flexibility to allow for reaping the benefits of parameter sharing and minimizing interference. Additionally, we show that by combining with an adaptive strategy, we can further improve the results. Experiments on two language directions show that our method outperforms or competes with other strong baselines on most latency values.
|
[
"Issam, Abderrahmane",
"Can Semerci, Yusuf",
"Scholtes, Jan",
"Spanakis, Gerasimos"
] |
Fixed and Adaptive Simultaneous Machine Translation Strategies Using Adapters
|
iwslt-1.36
|
Poster
|
2407.13469v1
|
https://aclanthology.org/2024.iwslt-1.37.bib
|
@inproceedings{singh-etal-2024-iwslt,
title = "{IWSLT} 2024 {I}ndic Track system description paper: Speech-to-Text Translation from {E}nglish to multiple Low-Resource {I}ndian Languages",
author = "Singh, Deepanjali and
Anand, Ayush and
Chaturvedi, Abhyuday and
Baliyan, Niyati",
editor = "Salesky, Elizabeth and
Federico, Marcello and
Carpuat, Marine",
booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand (in-person and online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.iwslt-1.37",
pages = "311--316",
abstract = "Our Speech-to-Text (ST) translation system addresses low-resource Indian languages (Hindi, Bengali, Tamil) by combining advanced transcription and translation models for accurate and efficient translations. The key components of the system are: The Audio Processor and Transcription Module which utilizes ResembleAI for noise reduction and OpenAI{'}s Whisper model for transcription. The Input Module validates and preprocesses audio files. The Translation Modules integrate the Helsinki-NLP model for English to Hindi translation and Facebook{'}s MBart model for English to Tamil and Bengali translations, fine-tuned for better quality. The Output Module corrects syntax and removes hallucinations, delivering the final translated text. For performance evaluation purpose, SacreBLEU scores were used and attained the following values: English-to-Hindi: 24.21 (baseline: 5.23); English-to-Bengali: 16.18 (baseline: 5.86); English-to-Tamil: 10.79 (baseline: 1.9). The solution streamlines workflow from input validation to output delivery, significantly enhancing communication across different linguistic contexts and achieving substantial improvements in SacreBLEU scores. Through the creation of dedicated datasets and the development of robust models, our aim is to facilitate seamless communication and accessibility across diverse linguistic communities, ultimately promoting inclusivity and empowerment.",
}
|
Our Speech-to-Text (ST) translation system addresses low-resource Indian languages (Hindi, Bengali, Tamil) by combining advanced transcription and translation models for accurate and efficient translations. The key components of the system are: The Audio Processor and Transcription Module which utilizes ResembleAI for noise reduction and OpenAI{'}s Whisper model for transcription. The Input Module validates and preprocesses audio files. The Translation Modules integrate the Helsinki-NLP model for English to Hindi translation and Facebook{'}s MBart model for English to Tamil and Bengali translations, fine-tuned for better quality. The Output Module corrects syntax and removes hallucinations, delivering the final translated text. For performance evaluation purpose, SacreBLEU scores were used and attained the following values: English-to-Hindi: 24.21 (baseline: 5.23); English-to-Bengali: 16.18 (baseline: 5.86); English-to-Tamil: 10.79 (baseline: 1.9). The solution streamlines workflow from input validation to output delivery, significantly enhancing communication across different linguistic contexts and achieving substantial improvements in SacreBLEU scores. Through the creation of dedicated datasets and the development of robust models, our aim is to facilitate seamless communication and accessibility across diverse linguistic communities, ultimately promoting inclusivity and empowerment.
|
[
"Singh, Deepanjali",
"An",
", Ayush",
"Chaturvedi, Abhyuday",
"Baliyan, Niyati"
] |
{IWSLT} 2024 {I}ndic Track system description paper: Speech-to-Text Translation from {E}nglish to multiple Low-Resource {I}ndian Languages
|
iwslt-1.37
|
Poster
|
1810.10320v1
|
https://aclanthology.org/2024.kallm-1.1.bib
|
@inproceedings{son-etal-2024-multi,
title = "Multi-hop Database Reasoning with Virtual Knowledge Graph",
author = "Son, Juhee and
Seonwoo, Yeon and
Oh, Alice and
Thorne, James and
Yoon, Seunghyun",
editor = "Biswas, Russa and
Kaffee, Lucie-Aim{\'e}e and
Agarwal, Oshin and
Minervini, Pasquale and
Singh, Sameer and
de Melo, Gerard",
booktitle = "Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.kallm-1.1",
pages = "1--11",
abstract = "Application of LLM to database queries on natural language sentences has demonstrated impressive results in both single and multi-hop scenarios.In the existing methodologies, the requirement to re-encode query vectors at each stage for processing multi-hop queries presents a significant bottleneck to the inference speed.This paper proposes VKGFR (Virtual Knowledge Graph based Fact Retriever) that leverages large language models to extract representations corresponding to a sentence{'}s knowledge graph, significantly enhancing inference speed for multi-hop reasoning without performance loss.Given that both the queries and natural language database sentences can be structured as a knowledge graph, we suggest extracting a Virtual Knowledge Graph (VKG) representation from sentences with LLM.Over the pre-constructed VKG, our VKGFR conducts retrieval with a tiny model structure, showing performance improvements with higher computational efficiency. We evaluate VKGFR on the WikiNLDB and MetaQA dataset, designed for multi-hop database reasoning over text. The results indicate 13x faster inference speed on the WikiNLDB dataset without performance loss.",
}
|
Application of LLM to database queries on natural language sentences has demonstrated impressive results in both single and multi-hop scenarios.In the existing methodologies, the requirement to re-encode query vectors at each stage for processing multi-hop queries presents a significant bottleneck to the inference speed.This paper proposes VKGFR (Virtual Knowledge Graph based Fact Retriever) that leverages large language models to extract representations corresponding to a sentence{'}s knowledge graph, significantly enhancing inference speed for multi-hop reasoning without performance loss.Given that both the queries and natural language database sentences can be structured as a knowledge graph, we suggest extracting a Virtual Knowledge Graph (VKG) representation from sentences with LLM.Over the pre-constructed VKG, our VKGFR conducts retrieval with a tiny model structure, showing performance improvements with higher computational efficiency. We evaluate VKGFR on the WikiNLDB and MetaQA dataset, designed for multi-hop database reasoning over text. The results indicate 13x faster inference speed on the WikiNLDB dataset without performance loss.
|
[
"Son, Juhee",
"Seonwoo, Yeon",
"Oh, Alice",
"Thorne, James",
"Yoon, Seunghyun"
] |
Multi-hop Database Reasoning with Virtual Knowledge Graph
|
kallm-1.1
|
Poster
|
2202.03173v2
|
https://aclanthology.org/2024.kallm-1.2.bib
|
@inproceedings{papaluca-etal-2024-zero,
title = "Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models",
author = "Papaluca, Andrea and
Krefl, Daniel and
Rodr{\'\i}guez M{\'e}ndez, Sergio and
Lensky, Artem and
Suominen, Hanna",
editor = "Biswas, Russa and
Kaffee, Lucie-Aim{\'e}e and
Agarwal, Oshin and
Minervini, Pasquale and
Singh, Sameer and
de Melo, Gerard",
booktitle = "Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.kallm-1.2",
pages = "12--23",
abstract = "In this work, we tested the Triplet Extraction (TE) capabilities of a variety of Large Language Models (LLMs) of different sizes in the Zero- and Few-Shots settings. In detail, we proposed a pipeline that dynamically gathers contextual information from a Knowledge Base (KB), both in the form of context triplets and of (sentence, triplets) pairs as examples, and provides it to the LLM through a prompt. The additional context allowed the LLMs to be competitive with all the older fully trained baselines based on the Bidirectional Long Short-Term Memory (BiLSTM) Network architecture. We further conducted a detailed analysis of the quality of the gathered KB context, finding it to be strongly correlated with the final TE performance of the model. In contrast, the size of the model appeared to only logarithmically improve the TE capabilities of the LLMs. We release the code on GitHub for reproducibility.",
}
|
In this work, we tested the Triplet Extraction (TE) capabilities of a variety of Large Language Models (LLMs) of different sizes in the Zero- and Few-Shots settings. In detail, we proposed a pipeline that dynamically gathers contextual information from a Knowledge Base (KB), both in the form of context triplets and of (sentence, triplets) pairs as examples, and provides it to the LLM through a prompt. The additional context allowed the LLMs to be competitive with all the older fully trained baselines based on the Bidirectional Long Short-Term Memory (BiLSTM) Network architecture. We further conducted a detailed analysis of the quality of the gathered KB context, finding it to be strongly correlated with the final TE performance of the model. In contrast, the size of the model appeared to only logarithmically improve the TE capabilities of the LLMs. We release the code on GitHub for reproducibility.
|
[
"Papaluca, Andrea",
"Krefl, Daniel",
"Rodr{\\'\\i}guez M{\\'e}ndez, Sergio",
"Lensky, Artem",
"Suominen, Hanna"
] |
Zero- and Few-Shots Knowledge Graph Triplet Extraction with Large Language Models
|
kallm-1.2
|
Poster
|
2312.01954v1
|
https://aclanthology.org/2024.kallm-1.3.bib
|
@inproceedings{ishii-etal-2024-analysis,
title = "Analysis of {LLM}{'}s {``}Spurious{''} Correct Answers Using Evidence Information of Multi-hop {QA} Datasets",
author = "Ishii, Ai and
Inoue, Naoya and
Suzuki, Hisami and
Sekine, Satoshi",
editor = "Biswas, Russa and
Kaffee, Lucie-Aim{\'e}e and
Agarwal, Oshin and
Minervini, Pasquale and
Singh, Sameer and
de Melo, Gerard",
booktitle = "Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.kallm-1.3",
pages = "24--34",
abstract = "Recent LLMs show an impressive accuracy on one of the hallmark tasks of language understanding, namely Question Answering (QA). However, it is not clear if the correct answers provided by LLMs are actually grounded on the correct knowledge related to the question. In this paper, we use multi-hop QA datasets to evaluate the accuracy of the knowledge LLMs use to answer questions, and show that as much as 31{\%} of the correct answers by the LLMs are in fact spurious, i.e., the knowledge LLMs used to ground the answer is wrong while the answer is correct. We present an analysis of these spurious correct answers by GPT-4 using three datasets in two languages, while suggesting future pathways to correct the grounding information using existing external knowledge bases.",
}
|
Recent LLMs show an impressive accuracy on one of the hallmark tasks of language understanding, namely Question Answering (QA). However, it is not clear if the correct answers provided by LLMs are actually grounded on the correct knowledge related to the question. In this paper, we use multi-hop QA datasets to evaluate the accuracy of the knowledge LLMs use to answer questions, and show that as much as 31{\%} of the correct answers by the LLMs are in fact spurious, i.e., the knowledge LLMs used to ground the answer is wrong while the answer is correct. We present an analysis of these spurious correct answers by GPT-4 using three datasets in two languages, while suggesting future pathways to correct the grounding information using existing external knowledge bases.
|
[
"Ishii, Ai",
"Inoue, Naoya",
"Suzuki, Hisami",
"Sekine, Satoshi"
] |
Analysis of {LLM}{'}s {``}Spurious{''} Correct Answers Using Evidence Information of Multi-hop {QA} Datasets
|
kallm-1.3
|
Poster
|
2404.12452v1
|
https://aclanthology.org/2024.kallm-1.4.bib
|
@inproceedings{mendes-etal-2024-application,
title = "Application of Generative {AI} as an Enterprise Wikibase Knowledge Graph {Q}{\&}{A} System",
author = "Mendes, Ren{\^e} and
Oliveira, Dimas and
Garcia, Victor",
editor = "Biswas, Russa and
Kaffee, Lucie-Aim{\'e}e and
Agarwal, Oshin and
Minervini, Pasquale and
Singh, Sameer and
de Melo, Gerard",
booktitle = "Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.kallm-1.4",
pages = "35--42",
abstract = "Generative AI and Large Language Models are increasingly used in business contexts. One application involves natural language conversations contextualized by company data, which can be accomplished by Enterprise Knowledge Graphs, standardized representations of data. This paper outlines an architecture for implementation of an Enterprise Knowledge Graph using open-source Wikibase software. Additionally, it is presented a Knowledge Graph Q{\&}A System powered by Generative AI.",
}
|
Generative AI and Large Language Models are increasingly used in business contexts. One application involves natural language conversations contextualized by company data, which can be accomplished by Enterprise Knowledge Graphs, standardized representations of data. This paper outlines an architecture for implementation of an Enterprise Knowledge Graph using open-source Wikibase software. Additionally, it is presented a Knowledge Graph Q{\&}A System powered by Generative AI.
|
[
"Mendes, Ren{\\^e}",
"Oliveira, Dimas",
"Garcia, Victor"
] |
Application of Generative {AI} as an Enterprise Wikibase Knowledge Graph {Q}{\&}{A} System
|
kallm-1.4
|
Poster
|
2006.01908v1
|
https://aclanthology.org/2024.kallm-1.5.bib
|
@inproceedings{vuth-etal-2024-kgast,
title = "{KGAST}: From Knowledge Graphs to Annotated Synthetic Texts",
author = "Vuth, Nakanyseth and
S{\'e}rasset, Gilles and
Schwab, Didier",
editor = "Biswas, Russa and
Kaffee, Lucie-Aim{\'e}e and
Agarwal, Oshin and
Minervini, Pasquale and
Singh, Sameer and
de Melo, Gerard",
booktitle = "Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.kallm-1.5",
pages = "43--55",
abstract = "In recent years, the use of synthetic data, either as a complement or a substitute for original data, has emerged as a solution to challenges such as data scarcity and security risks. This paper is an initial attempt to automatically generate such data for Information Extraction tasks. We accomplished this by developing a novel synthetic data generation framework called KGAST, which leverages Knowledge Graphs and Large Language Models. In our preliminary study, we conducted simple experiments to generate synthetic versions of two datasets{---}a French security defense dataset and an English general domain dataset, after which we evaluated them both intrinsically and extrinsically. The results indicated that synthetic data can effectively complement original data, improving the performance of models on classes with limited training samples. This highlights KGAST{'}s potential as a tool for generating synthetic data for Information Extraction tasks.",
}
|
In recent years, the use of synthetic data, either as a complement or a substitute for original data, has emerged as a solution to challenges such as data scarcity and security risks. This paper is an initial attempt to automatically generate such data for Information Extraction tasks. We accomplished this by developing a novel synthetic data generation framework called KGAST, which leverages Knowledge Graphs and Large Language Models. In our preliminary study, we conducted simple experiments to generate synthetic versions of two datasets{---}a French security defense dataset and an English general domain dataset, after which we evaluated them both intrinsically and extrinsically. The results indicated that synthetic data can effectively complement original data, improving the performance of models on classes with limited training samples. This highlights KGAST{'}s potential as a tool for generating synthetic data for Information Extraction tasks.
|
[
"Vuth, Nakanyseth",
"S{\\'e}rasset, Gilles",
"Schwab, Didier"
] |
{KGAST}: From Knowledge Graphs to Annotated Synthetic Texts
|
kallm-1.5
|
Poster
|
2209.10754v1
|
https://aclanthology.org/2024.kallm-1.6.bib
|
@inproceedings{wasi-2024-hrgraph,
title = "{HRG}raph: Leveraging {LLM}s for {HR} Data Knowledge Graphs with Information Propagation-based Job Recommendation",
author = "Wasi, Azmine Toushik",
editor = "Biswas, Russa and
Kaffee, Lucie-Aim{\'e}e and
Agarwal, Oshin and
Minervini, Pasquale and
Singh, Sameer and
de Melo, Gerard",
booktitle = "Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.kallm-1.6",
pages = "56--62",
abstract = "Knowledge Graphs (KGs) serving as semantic networks, prove highly effective in managing complex interconnected data in different domains, by offering a unified, contextualized, and structured representation with flexibility that allows for easy adaptation to evolving knowledge. Processing complex Human Resources (HR) data, KGs can help in different HR functions like recruitment, job matching, identifying learning gaps, and enhancing employee retention. Despite their potential, limited efforts have been made to implement practical HR knowledge graphs. This study addresses this gap by presenting a framework for effectively developing HR knowledge graphs from documents using Large Language Models. The resulting KG can be used for a variety of downstream tasks, including job matching, identifying employee skill gaps, and many more. In this work, we showcase instances where HR KGs prove instrumental in precise job matching, yielding advantages for both employers and employees. Empirical evidence from experiments with information propagation in KGs and Graph Neural Nets, along with case studies underscores the effectiveness of KGs in tasks such as job and employee recommendations and job area classification. Code and data are available at : https://github.com/azminewasi/HRGraph",
}
|
Knowledge Graphs (KGs) serving as semantic networks, prove highly effective in managing complex interconnected data in different domains, by offering a unified, contextualized, and structured representation with flexibility that allows for easy adaptation to evolving knowledge. Processing complex Human Resources (HR) data, KGs can help in different HR functions like recruitment, job matching, identifying learning gaps, and enhancing employee retention. Despite their potential, limited efforts have been made to implement practical HR knowledge graphs. This study addresses this gap by presenting a framework for effectively developing HR knowledge graphs from documents using Large Language Models. The resulting KG can be used for a variety of downstream tasks, including job matching, identifying employee skill gaps, and many more. In this work, we showcase instances where HR KGs prove instrumental in precise job matching, yielding advantages for both employers and employees. Empirical evidence from experiments with information propagation in KGs and Graph Neural Nets, along with case studies underscores the effectiveness of KGs in tasks such as job and employee recommendations and job area classification. Code and data are available at : https://github.com/azminewasi/HRGraph
|
[
"Wasi, Azmine Toushik"
] |
{HRG}raph: Leveraging {LLM}s for {HR} Data Knowledge Graphs with Information Propagation-based Job Recommendation
|
kallm-1.6
|
Poster
|
2405.12442v1
|
https://aclanthology.org/2024.kallm-1.7.bib
|
@inproceedings{gurgurov-etal-2024-adapting,
title = "Adapting Multilingual {LLM}s to Low-Resource Languages with Knowledge Graphs via Adapters",
author = "Gurgurov, Daniil and
Hartmann, Mareike and
Ostermann, Simon",
editor = "Biswas, Russa and
Kaffee, Lucie-Aim{\'e}e and
Agarwal, Oshin and
Minervini, Pasquale and
Singh, Sameer and
de Melo, Gerard",
booktitle = "Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.kallm-1.7",
pages = "63--74",
abstract = "This paper explores the integration of graph knowledge from linguistic ontologies into multilingual Large Language Models (LLMs) using adapters to improve performance for low-resource languages (LRLs) in sentiment analysis (SA) and named entity recognition (NER). Building upon successful parameter-efficient fine-tuning techniques, such as K-ADAPTER and MAD-X, we propose a similar approach for incorporating knowledge from multilingual graphs, connecting concepts in various languages with each other through linguistic relationships, into multilingual LLMs for LRLs. Specifically, we focus on eight LRLs {---} Maltese, Bulgarian, Indonesian, Nepali, Javanese, Uyghur, Tibetan, and Sinhala {---} and employ language-specific adapters fine-tuned on data extracted from the language-specific section of ConceptNet, aiming to enable knowledge transfer across the languages covered by the knowledge graph. We compare various fine-tuning objectives, including standard Masked Language Modeling (MLM), MLM with full-word masking, and MLM with targeted masking, to analyze their effectiveness in learning and integrating the extracted graph data. Through empirical evaluation on language-specific tasks, we assess how structured graph knowledge affects the performance of multilingual LLMs for LRLs in SA and NER, providing insights into the potential benefits of adapting language models for low-resource scenarios.",
}
|
This paper explores the integration of graph knowledge from linguistic ontologies into multilingual Large Language Models (LLMs) using adapters to improve performance for low-resource languages (LRLs) in sentiment analysis (SA) and named entity recognition (NER). Building upon successful parameter-efficient fine-tuning techniques, such as K-ADAPTER and MAD-X, we propose a similar approach for incorporating knowledge from multilingual graphs, connecting concepts in various languages with each other through linguistic relationships, into multilingual LLMs for LRLs. Specifically, we focus on eight LRLs {---} Maltese, Bulgarian, Indonesian, Nepali, Javanese, Uyghur, Tibetan, and Sinhala {---} and employ language-specific adapters fine-tuned on data extracted from the language-specific section of ConceptNet, aiming to enable knowledge transfer across the languages covered by the knowledge graph. We compare various fine-tuning objectives, including standard Masked Language Modeling (MLM), MLM with full-word masking, and MLM with targeted masking, to analyze their effectiveness in learning and integrating the extracted graph data. Through empirical evaluation on language-specific tasks, we assess how structured graph knowledge affects the performance of multilingual LLMs for LRLs in SA and NER, providing insights into the potential benefits of adapting language models for low-resource scenarios.
|
[
"Gurgurov, Daniil",
"Hartmann, Mareike",
"Ostermann, Simon"
] |
Adapting Multilingual {LLM}s to Low-Resource Languages with Knowledge Graphs via Adapters
|
kallm-1.7
|
Poster
|
2407.01406v2
|
https://aclanthology.org/2024.kallm-1.8.bib
|
@inproceedings{cauter-yakovets-2024-ontology,
title = "Ontology-guided Knowledge Graph Construction from Maintenance Short Texts",
author = "Cauter, Zeno and
Yakovets, Nikolay",
editor = "Biswas, Russa and
Kaffee, Lucie-Aim{\'e}e and
Agarwal, Oshin and
Minervini, Pasquale and
Singh, Sameer and
de Melo, Gerard",
booktitle = "Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.kallm-1.8",
pages = "75--84",
abstract = "Large-scale knowledge graph construction remains infeasible since it requires significant human-expert involvement. Further complications arise when building graphs from domain-specific data due to their unique vocabularies and associated contexts. In this work, we demonstrate the ability of open-source large language models (LLMs), such as Llama-2 and Llama-3, to extract facts from domain-specific Maintenance Short Texts (MSTs). We employ an approach which combines ontology-guided triplet extraction and in-context learning. By using only 20 semantically similar examples with the Llama-3-70B-Instruct model, we achieve performance comparable to previous methods that relied on fine-tuning techniques like SpERT and REBEL. This indicates that domain-specific fact extraction can be accomplished through inference alone, requiring minimal labeled data. This opens up possibilities for effective and efficient semi-automated knowledge graph construction for domain-specific data.",
}
|
Large-scale knowledge graph construction remains infeasible since it requires significant human-expert involvement. Further complications arise when building graphs from domain-specific data due to their unique vocabularies and associated contexts. In this work, we demonstrate the ability of open-source large language models (LLMs), such as Llama-2 and Llama-3, to extract facts from domain-specific Maintenance Short Texts (MSTs). We employ an approach which combines ontology-guided triplet extraction and in-context learning. By using only 20 semantically similar examples with the Llama-3-70B-Instruct model, we achieve performance comparable to previous methods that relied on fine-tuning techniques like SpERT and REBEL. This indicates that domain-specific fact extraction can be accomplished through inference alone, requiring minimal labeled data. This opens up possibilities for effective and efficient semi-automated knowledge graph construction for domain-specific data.
|
[
"Cauter, Zeno",
"Yakovets, Nikolay"
] |
Ontology-guided Knowledge Graph Construction from Maintenance Short Texts
|
kallm-1.8
|
Poster
|
2403.19856v1
|
https://aclanthology.org/2024.kallm-1.9.bib
|
@inproceedings{canal-esteve-gutierrez-2024-educational,
title = "Educational Material to Knowledge Graph Conversion: A Methodology to Enhance Digital Education",
author = "Canal-Esteve, Miquel and
Gutierrez, Yoan",
editor = "Biswas, Russa and
Kaffee, Lucie-Aim{\'e}e and
Agarwal, Oshin and
Minervini, Pasquale and
Singh, Sameer and
de Melo, Gerard",
booktitle = "Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.kallm-1.9",
pages = "85--91",
abstract = "This article argues that digital educational content should be structured as knowledge graphs (KGs). Unlike traditional repositories such as Moodle, a KG offers a more flexible representation of the relationships between concepts, facilitating intuitive navigation and discovery of connections. In addition, it integrates effectively with Large Language Models, enhancing personalized explanations, answers, and recommendations. This article studies different proposals based on semantics and knowledge modelling to determine the most appropriate ways to strengthen intelligent educational technologies.",
}
|
This article argues that digital educational content should be structured as knowledge graphs (KGs). Unlike traditional repositories such as Moodle, a KG offers a more flexible representation of the relationships between concepts, facilitating intuitive navigation and discovery of connections. In addition, it integrates effectively with Large Language Models, enhancing personalized explanations, answers, and recommendations. This article studies different proposals based on semantics and knowledge modelling to determine the most appropriate ways to strengthen intelligent educational technologies.
|
[
"Canal-Esteve, Miquel",
"Gutierrez, Yoan"
] |
Educational Material to Knowledge Graph Conversion: A Methodology to Enhance Digital Education
|
kallm-1.9
|
Poster
|
2403.12071v1
|
https://aclanthology.org/2024.kallm-1.10.bib
|
@inproceedings{zolnai-lucas-etal-2024-stage,
title = "{STAGE}: Simplified Text-Attributed Graph Embeddings using Pre-trained {LLM}s",
author = "Zolnai-Lucas, Aaron and
Boylan, Jack and
Hokamp, Chris and
Ghaffari, Parsa",
editor = "Biswas, Russa and
Kaffee, Lucie-Aim{\'e}e and
Agarwal, Oshin and
Minervini, Pasquale and
Singh, Sameer and
de Melo, Gerard",
booktitle = "Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.kallm-1.10",
pages = "92--104",
abstract = "We present STAGE, a straightforward yet effective method for enhancing node features in Graph Neural Network (GNN) models that encode Text-Attributed Graphs (TAGs). Our approach leverages Large-Language Models (LLMs) to generate embeddings for textual attributes. STAGE achieves competitive results on various node classification benchmarks while also maintaining a simplicity in implementation relative to current state-of-the-art (SoTA) techniques. We show that utilizing pre-trained LLMs as embedding generators provides robust features for ensemble GNN training, enabling pipelines that are simpler than current SoTA approaches which require multiple expensive training and prompting stages. We also implement diffusion-pattern GNNs in an effort to make this pipeline scalable to graphs beyond academic benchmarks.",
}
|
We present STAGE, a straightforward yet effective method for enhancing node features in Graph Neural Network (GNN) models that encode Text-Attributed Graphs (TAGs). Our approach leverages Large-Language Models (LLMs) to generate embeddings for textual attributes. STAGE achieves competitive results on various node classification benchmarks while also maintaining a simplicity in implementation relative to current state-of-the-art (SoTA) techniques. We show that utilizing pre-trained LLMs as embedding generators provides robust features for ensemble GNN training, enabling pipelines that are simpler than current SoTA approaches which require multiple expensive training and prompting stages. We also implement diffusion-pattern GNNs in an effort to make this pipeline scalable to graphs beyond academic benchmarks.
|
[
"Zolnai-Lucas, Aaron",
"Boylan, Jack",
"Hokamp, Chris",
"Ghaffari, Parsa"
] |
{STAGE}: Simplified Text-Attributed Graph Embeddings using Pre-trained {LLM}s
|
kallm-1.10
|
Poster
|
2312.04737v1
|
https://aclanthology.org/2024.kallm-1.11.bib
|
@inproceedings{yuan-vlachos-2024-zero,
title = "Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphs",
author = "Yuan, Moy and
Vlachos, Andreas",
editor = "Biswas, Russa and
Kaffee, Lucie-Aim{\'e}e and
Agarwal, Oshin and
Minervini, Pasquale and
Singh, Sameer and
de Melo, Gerard",
booktitle = "Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.kallm-1.11",
pages = "105--115",
abstract = "Despite progress in automated fact-checking, most systems require a significant amount of labeled training data, which is expensive. In this paper, we propose a novel zero-shot method, which instead of operating directly on the claim and evidence sentences, decomposes them into semantic triples augmented using external knowledge graphs, and uses large language models trained for natural language inference. This allows it to generalize to adversarial datasets and domains that supervised models require specific training data for. Our empirical results show that our approach outperforms previous zero-shot approaches on FEVER, FEVER-Symmetric, FEVER 2.0, and Climate-FEVER, while being comparable or better than supervised models on the adversarial and the out-of-domain datasets.",
}
|
Despite progress in automated fact-checking, most systems require a significant amount of labeled training data, which is expensive. In this paper, we propose a novel zero-shot method, which instead of operating directly on the claim and evidence sentences, decomposes them into semantic triples augmented using external knowledge graphs, and uses large language models trained for natural language inference. This allows it to generalize to adversarial datasets and domains that supervised models require specific training data for. Our empirical results show that our approach outperforms previous zero-shot approaches on FEVER, FEVER-Symmetric, FEVER 2.0, and Climate-FEVER, while being comparable or better than supervised models on the adversarial and the out-of-domain datasets.
|
[
"Yuan, Moy",
"Vlachos, Andreas"
] |
Zero-Shot Fact-Checking with Semantic Triples and Knowledge Graphs
|
kallm-1.11
|
Poster
|
1905.02497v2
|
https://aclanthology.org/2024.kallm-1.12.bib
|
@inproceedings{zhang-etal-2024-fine-tuning,
title = "Fine-tuning Language Models for Triple Extraction with Data Augmentation",
author = "Zhang, Yujia and
Sadler, Tyler and
Taesiri, Mohammad Reza and
Xu, Wenjie and
Reformat, Marek",
editor = "Biswas, Russa and
Kaffee, Lucie-Aim{\'e}e and
Agarwal, Oshin and
Minervini, Pasquale and
Singh, Sameer and
de Melo, Gerard",
booktitle = "Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.kallm-1.12",
pages = "116--124",
abstract = "Advanced language models with impressive capabilities to process textual information can more effectively extract high-quality triples, which are the building blocks of knowledge graphs. Our work examines language models{'} abilities to extract entities and the relationships between them. We use a diverse data augmentation process to fine-tune large language models to extract triples from the text. Fine-tuning is performed using a mix of trainers from HuggingFace and five public datasets, such as different variations of the WebNLG, SKE, DocRed, FewRel, and KELM. Evaluation involves comparing model outputs with test-set triples based on several criteria, such as type, partial, exact, and strict accuracy.The obtained results outperform ChatGPT and even match or exceed the performance of GPT-4.",
}
|
Advanced language models with impressive capabilities to process textual information can more effectively extract high-quality triples, which are the building blocks of knowledge graphs. Our work examines language models{'} abilities to extract entities and the relationships between them. We use a diverse data augmentation process to fine-tune large language models to extract triples from the text. Fine-tuning is performed using a mix of trainers from HuggingFace and five public datasets, such as different variations of the WebNLG, SKE, DocRed, FewRel, and KELM. Evaluation involves comparing model outputs with test-set triples based on several criteria, such as type, partial, exact, and strict accuracy.The obtained results outperform ChatGPT and even match or exceed the performance of GPT-4.
|
[
"Zhang, Yujia",
"Sadler, Tyler",
"Taesiri, Mohammad Reza",
"Xu, Wenjie",
"Reformat, Marek"
] |
Fine-tuning Language Models for Triple Extraction with Data Augmentation
|
kallm-1.12
|
Poster
|
2205.07830v1
|
https://aclanthology.org/2024.kallm-1.13.bib
|
@inproceedings{shah-tian-2024-improving,
title = "Improving {LLM}-based {KGQA} for multi-hop Question Answering with implicit reasoning in few-shot examples",
author = "Shah, Mili and
Tian, Jing",
editor = "Biswas, Russa and
Kaffee, Lucie-Aim{\'e}e and
Agarwal, Oshin and
Minervini, Pasquale and
Singh, Sameer and
de Melo, Gerard",
booktitle = "Proceedings of the 1st Workshop on Knowledge Graphs and Large Language Models (KaLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.kallm-1.13",
pages = "125--135",
abstract = "Large language models (LLMs) have shown remarkable capabilities in generating natural language texts for various tasks. However, using LLMs for question answering on knowledge graphs still remains a challenge, especially for questions requiring multi-hop reasoning. In this paper, we present a novel planned query guidance approach that improves large language model (LLM) performance in multi-hop question answering on knowledge graphs (KGQA). We do this by designing few-shot examples that implicitly demonstrate a systematic reasoning methodology to answer multi-hop questions. We evaluate our approach for two graph query languages, Cypher and SPARQL, and show that the queries generated using our strategy outperform the queries generated using a baseline LLM and typical few-shot examples by up to 24.66{\%} and 7.7{\%} in execution match accuracy for the MetaQA and the Spider benchmarks respectively. We also conduct an ablation study to analyze the incremental effects of the different techniques of designing few-shot examples. Our results suggest that our approach enables the LLM to effectively leverage the few-shot examples to generate queries for multi-hop KGQA.",
}
|
Large language models (LLMs) have shown remarkable capabilities in generating natural language texts for various tasks. However, using LLMs for question answering on knowledge graphs still remains a challenge, especially for questions requiring multi-hop reasoning. In this paper, we present a novel planned query guidance approach that improves large language model (LLM) performance in multi-hop question answering on knowledge graphs (KGQA). We do this by designing few-shot examples that implicitly demonstrate a systematic reasoning methodology to answer multi-hop questions. We evaluate our approach for two graph query languages, Cypher and SPARQL, and show that the queries generated using our strategy outperform the queries generated using a baseline LLM and typical few-shot examples by up to 24.66{\%} and 7.7{\%} in execution match accuracy for the MetaQA and the Spider benchmarks respectively. We also conduct an ablation study to analyze the incremental effects of the different techniques of designing few-shot examples. Our results suggest that our approach enables the LLM to effectively leverage the few-shot examples to generate queries for multi-hop KGQA.
|
[
"Shah, Mili",
"Tian, Jing"
] |
Improving {LLM}-based {KGQA} for multi-hop Question Answering with implicit reasoning in few-shot examples
|
kallm-1.13
|
Poster
|
2403.01395v1
|
https://aclanthology.org/2024.knowledgenlp-1.1.bib
|
@inproceedings{coman-etal-2024-gadepo,
title = "{GAD}e{P}o: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction",
author = "Coman, Andrei and
Theodoropoulos, Christos and
Moens, Marie-Francine and
Henderson, James",
editor = "Yu, Wenhao and
Shi, Weijia and
Yasunaga, Michihiro and
Jiang, Meng and
Zhu, Chenguang and
Hajishirzi, Hannaneh and
Zettlemoyer, Luke and
Zhang, Zhihan",
booktitle = "Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowledgenlp-1.1",
pages = "1--14",
abstract = "Document-level relation extraction typically relies on text-based encoders and hand-coded pooling heuristics to aggregate information learned by the encoder. In this paper, we leverage the intrinsic graph processing capabilities of the Transformer model and propose replacing hand-coded pooling methods with new tokens in the input, which are designed to aggregate information via explicit graph relations in the computation of attention weights. We introduce a joint text-graph Transformer model and a graph-assisted declarative pooling (GADePo) specification of the input, which provides explicit and high-level instructions for information aggregation. GADePo allows the pooling process to be guided by domain-specific knowledge or desired outcomes but still learned by the Transformer, leading to more flexible and customisable pooling strategies. We evaluate our method across diverse datasets and models and show that our approach yields promising results that are consistently better than those achieved by the hand-coded pooling functions.",
}
|
Document-level relation extraction typically relies on text-based encoders and hand-coded pooling heuristics to aggregate information learned by the encoder. In this paper, we leverage the intrinsic graph processing capabilities of the Transformer model and propose replacing hand-coded pooling methods with new tokens in the input, which are designed to aggregate information via explicit graph relations in the computation of attention weights. We introduce a joint text-graph Transformer model and a graph-assisted declarative pooling (GADePo) specification of the input, which provides explicit and high-level instructions for information aggregation. GADePo allows the pooling process to be guided by domain-specific knowledge or desired outcomes but still learned by the Transformer, leading to more flexible and customisable pooling strategies. We evaluate our method across diverse datasets and models and show that our approach yields promising results that are consistently better than those achieved by the hand-coded pooling functions.
|
[
"Coman, Andrei",
"Theodoropoulos, Christos",
"Moens, Marie-Francine",
"Henderson, James"
] |
{GAD}e{P}o: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction
|
knowledgenlp-1.1
|
Poster
|
2308.14423v4
|
https://aclanthology.org/2024.knowledgenlp-1.2.bib
|
@inproceedings{eppalapally-etal-2024-kapqa,
title = "{K}a{PQA}: Knowledge-Augmented Product Question-Answering",
author = "Eppalapally, Swetha and
Dangi, Daksh and
Bhat, Chaithra and
Gupta, Ankita and
Zhang, Ruiyi and
Agarwal, Shubham and
Bagga, Karishma and
Yoon, Seunghyun and
Lipka, Nedim and
Rossi, Ryan and
Dernoncourt, Franck",
editor = "Yu, Wenhao and
Shi, Weijia and
Yasunaga, Michihiro and
Jiang, Meng and
Zhu, Chenguang and
Hajishirzi, Hannaneh and
Zettlemoyer, Luke and
Zhang, Zhihan",
booktitle = "Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowledgenlp-1.2",
pages = "15--29",
abstract = "Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs). However, accurately assessing the performance of these applications remains a challenge, mainly due to the lack of suitable benchmarks that effectively simulate real-world scenarios. To address this challenge, we introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products to help evaluate the performance of existing models on domain-specific product QA tasks. Additionally, we propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task. Our experiments demonstrated that inducing domain knowledge through query reformulation allowed for increased retrieval and generative performance when compared to standard RAG-QA methods. This improvement, however, is slight, and thus illustrates the challenge posed by the datasets introduced.",
}
|
Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs). However, accurately assessing the performance of these applications remains a challenge, mainly due to the lack of suitable benchmarks that effectively simulate real-world scenarios. To address this challenge, we introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products to help evaluate the performance of existing models on domain-specific product QA tasks. Additionally, we propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task. Our experiments demonstrated that inducing domain knowledge through query reformulation allowed for increased retrieval and generative performance when compared to standard RAG-QA methods. This improvement, however, is slight, and thus illustrates the challenge posed by the datasets introduced.
|
[
"Eppalapally, Swetha",
"Dangi, Daksh",
"Bhat, Chaithra",
"Gupta, Ankita",
"Zhang, Ruiyi",
"Agarwal, Shubham",
"Bagga, Karishma",
"Yoon, Seunghyun",
"Lipka, Nedim",
"Rossi, Ryan",
"Dernoncourt, Franck"
] |
{K}a{PQA}: Knowledge-Augmented Product Question-Answering
|
knowledgenlp-1.2
|
Poster
|
2307.04412v1
|
https://aclanthology.org/2024.knowledgenlp-1.3.bib
|
@inproceedings{collins-etal-2024-collecting,
title = "Collecting High-quality Multi-modal Conversational Search Data for {E}-Commerce",
author = "Collins, Marcus and
Rokhlenko, Oleg and
Agichtein, Eugene and
Malmasi, Shervin",
editor = "Yu, Wenhao and
Shi, Weijia and
Yasunaga, Michihiro and
Jiang, Meng and
Zhu, Chenguang and
Hajishirzi, Hannaneh and
Zettlemoyer, Luke and
Zhang, Zhihan",
booktitle = "Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowledgenlp-1.3",
pages = "30--43",
abstract = "Continued improvement of conversational assistants in knowledge-rich domains like E-Commerce requires large volumes of realistic high-quality conversation data to power increasingly sophisticated large language model chatbots, dialogue managers, response rankers, and recommenders. The problem is exacerbated for multi-modal interactions in realistic conversational product search and recommendation. Here, an artificial sales agent must interact intelligently with a customer using both textual and visual information and incorporate results from external search systems, such as a product catalog. Yet, it remains an open question how to best crowd-source large-scale, naturalistic multi-modal dialogue and action data, required to train such an artificial agent. We describe our crowd-sourced task where one worker (the Buyer) plays the role of the customer, and another (the Seller) plays the role of the sales agent. We identify subtle interactions between one worker{'}s environment and their partner{'}s behavior mediated by workers{'} word choice. We find that limiting information presented to the Buyer, both in their backstory and by the Seller, improves conversation quality. We also show how conversations are improved through minimal automated Seller {``}coaching{''}. While typed and spoken messages are slightly different, the differences are not as large as frequently assumed. We plan to release our platform code and the resulting dialogues to advance research on conversational search agents.",
}
|
Continued improvement of conversational assistants in knowledge-rich domains like E-Commerce requires large volumes of realistic high-quality conversation data to power increasingly sophisticated large language model chatbots, dialogue managers, response rankers, and recommenders. The problem is exacerbated for multi-modal interactions in realistic conversational product search and recommendation. Here, an artificial sales agent must interact intelligently with a customer using both textual and visual information and incorporate results from external search systems, such as a product catalog. Yet, it remains an open question how to best crowd-source large-scale, naturalistic multi-modal dialogue and action data, required to train such an artificial agent. We describe our crowd-sourced task where one worker (the Buyer) plays the role of the customer, and another (the Seller) plays the role of the sales agent. We identify subtle interactions between one worker{'}s environment and their partner{'}s behavior mediated by workers{'} word choice. We find that limiting information presented to the Buyer, both in their backstory and by the Seller, improves conversation quality. We also show how conversations are improved through minimal automated Seller {``}coaching{''}. While typed and spoken messages are slightly different, the differences are not as large as frequently assumed. We plan to release our platform code and the resulting dialogues to advance research on conversational search agents.
|
[
"Collins, Marcus",
"Rokhlenko, Oleg",
"Agichtein, Eugene",
"Malmasi, Shervin"
] |
Collecting High-quality Multi-modal Conversational Search Data for {E}-Commerce
|
knowledgenlp-1.3
|
Poster
|
2304.12636v1
|
https://aclanthology.org/2024.knowledgenlp-1.4.bib
|
@inproceedings{liang-etal-2024-learning,
title = "Learning to Trust Your Feelings: Leveraging Self-awareness in {LLM}s for Hallucination Mitigation",
author = "Liang, Yuxin and
Song, Zhuoyang and
Wang, Hao and
Zhang, Jiaxing",
editor = "Yu, Wenhao and
Shi, Weijia and
Yasunaga, Michihiro and
Jiang, Meng and
Zhu, Chenguang and
Hajishirzi, Hannaneh and
Zettlemoyer, Luke and
Zhang, Zhihan",
booktitle = "Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowledgenlp-1.4",
pages = "44--58",
abstract = "We evaluate the ability of Large Language Models (LLMs) to discern and express their internal knowledge state, a key factor in countering factual hallucination and ensuring reliable application of LLMs. We observe a robust self-awareness of internal knowledge state in LLMs, evidenced by over 85{\%} accuracy in knowledge state probing. However, LLMs often fail to faithfully express their internal knowledge during generation, leading to factual hallucinations. We develop an automated hallucination annotation tool, DreamCatcher, which merges knowledge probing and consistency checking methods to rank factual preference data. Using knowledge preference as reward, We propose a Reinforcement Learning from Knowledge Feedback (RLKF) training framework, leveraging reinforcement learning to enhance the factuality and honesty of LLMs. Our experiments across multiple models show that RLKF training effectively enhances the ability of models to utilize their internal knowledge state, boosting performance in a variety of knowledge-based and honesty-related tasks.",
}
|
We evaluate the ability of Large Language Models (LLMs) to discern and express their internal knowledge state, a key factor in countering factual hallucination and ensuring reliable application of LLMs. We observe a robust self-awareness of internal knowledge state in LLMs, evidenced by over 85{\%} accuracy in knowledge state probing. However, LLMs often fail to faithfully express their internal knowledge during generation, leading to factual hallucinations. We develop an automated hallucination annotation tool, DreamCatcher, which merges knowledge probing and consistency checking methods to rank factual preference data. Using knowledge preference as reward, We propose a Reinforcement Learning from Knowledge Feedback (RLKF) training framework, leveraging reinforcement learning to enhance the factuality and honesty of LLMs. Our experiments across multiple models show that RLKF training effectively enhances the ability of models to utilize their internal knowledge state, boosting performance in a variety of knowledge-based and honesty-related tasks.
|
[
"Liang, Yuxin",
"Song, Zhuoyang",
"Wang, Hao",
"Zhang, Jiaxing"
] |
Learning to Trust Your Feelings: Leveraging Self-awareness in {LLM}s for Hallucination Mitigation
|
knowledgenlp-1.4
|
Poster
|
2401.15449v1
|
https://aclanthology.org/2024.knowledgenlp-1.5.bib
|
@inproceedings{yokoyama-etal-2024-aggregating,
title = "Aggregating Impressions on Celebrities and their Reasons from Microblog Posts and Web Search Pages",
author = "Yokoyama, Hibiki and
Tsuchida, Rikuto and
Buma, Kosei and
Miyakawa, Sho and
Utsuro, Takehito and
Yoshioka, Masaharu",
editor = "Yu, Wenhao and
Shi, Weijia and
Yasunaga, Michihiro and
Jiang, Meng and
Zhu, Chenguang and
Hajishirzi, Hannaneh and
Zettlemoyer, Luke and
Zhang, Zhihan",
booktitle = "Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowledgenlp-1.5",
pages = "59--72",
abstract = "This paper aims to augment fans{'} ability to critique and exploreinformation related to celebrities of interest. First, we collect postsfrom X (formerly Twitter) that discuss matters related to specificcelebrities. For the collection of major impressions from these posts,we employ ChatGPT as a large language model (LLM) to analyze andsummarize key sentiments. Next, based on collected impressions, wesearch for Web pages and collect the content of the top 30 ranked pagesas the source for exploring the reasons behind those impressions. Oncethe Web page content collection is complete, we collect and aggregatedetailed reasons for the impressions on the celebrities from the contentof each page. For this part, we continue to use ChatGPT, enhanced bythe retrieval augmented generation (RAG) framework, to ensure thereliability of the collected results compared to relying solely on theprior knowledge of the LLM. Evaluation results by comparing a referencethat is manually collected and aggregated reasons with those predictedby ChatGPT revealed that ChatGPT achieves high accuracy in reasoncollection and aggregation. Furthermore, we compared the performance ofChatGPT with an existing model of mT5 in reason collection and confirmedthat ChatGPT exhibits superior performance.",
}
|
This paper aims to augment fans{'} ability to critique and exploreinformation related to celebrities of interest. First, we collect postsfrom X (formerly Twitter) that discuss matters related to specificcelebrities. For the collection of major impressions from these posts,we employ ChatGPT as a large language model (LLM) to analyze andsummarize key sentiments. Next, based on collected impressions, wesearch for Web pages and collect the content of the top 30 ranked pagesas the source for exploring the reasons behind those impressions. Oncethe Web page content collection is complete, we collect and aggregatedetailed reasons for the impressions on the celebrities from the contentof each page. For this part, we continue to use ChatGPT, enhanced bythe retrieval augmented generation (RAG) framework, to ensure thereliability of the collected results compared to relying solely on theprior knowledge of the LLM. Evaluation results by comparing a referencethat is manually collected and aggregated reasons with those predictedby ChatGPT revealed that ChatGPT achieves high accuracy in reasoncollection and aggregation. Furthermore, we compared the performance ofChatGPT with an existing model of mT5 in reason collection and confirmedthat ChatGPT exhibits superior performance.
|
[
"Yokoyama, Hibiki",
"Tsuchida, Rikuto",
"Buma, Kosei",
"Miyakawa, Sho",
"Utsuro, Takehito",
"Yoshioka, Masaharu"
] |
Aggregating Impressions on Celebrities and their Reasons from Microblog Posts and Web Search Pages
|
knowledgenlp-1.5
|
Poster
|
1801.03710v1
|
https://aclanthology.org/2024.knowledgenlp-1.6.bib
|
@inproceedings{hwang-etal-2024-dslr,
title = "{DSLR}: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation",
author = "Hwang, Taeho and
Jeong, Soyeong and
Cho, Sukmin and
Han, SeungYoon and
Park, Jong",
editor = "Yu, Wenhao and
Shi, Weijia and
Yasunaga, Michihiro and
Jiang, Meng and
Zhu, Chenguang and
Hajishirzi, Hannaneh and
Zettlemoyer, Luke and
Zhang, Zhihan",
booktitle = "Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowledgenlp-1.6",
pages = "73--92",
abstract = "Recent advancements in Large Language Models (LLMs) have significantly improved their performance across various Natural Language Processing (NLP) tasks.However, LLMs still struggle with generating non-factual responses due to limitations in their parametric memory.Retrieval-Augmented Generation (RAG) systems address this issue by incorporating external knowledge with a retrieval module.Despite their successes, however, current RAG systems face challenges with retrieval failures and the limited ability of LLMs to filter out irrelevant information.Therefore, in this work, we propose \textit{ \textbf{DSLR}} (\textbf{D}ocument Refinement with \textbf{S}entence-\textbf{L}evel \textbf{R}e-ranking and Reconstruction), an unsupervised framework that decomposes retrieved documents into sentences, filters out irrelevant sentences, and reconstructs them again into coherent passages.We experimentally validate \textit{DSLR} on multiple open-domain QA datasets and the results demonstrate that \textit{DSLR} significantly enhances the RAG performance over conventional fixed-size passage.Furthermore, our \textit{DSLR} enhances performance in specific, yet realistic scenarios without the need for additional training, providing an effective and efficient solution for refining retrieved documents in RAG systems.",
}
|
Recent advancements in Large Language Models (LLMs) have significantly improved their performance across various Natural Language Processing (NLP) tasks.However, LLMs still struggle with generating non-factual responses due to limitations in their parametric memory.Retrieval-Augmented Generation (RAG) systems address this issue by incorporating external knowledge with a retrieval module.Despite their successes, however, current RAG systems face challenges with retrieval failures and the limited ability of LLMs to filter out irrelevant information.Therefore, in this work, we propose \textit{ \textbf{DSLR}} (\textbf{D}ocument Refinement with \textbf{S}entence-\textbf{L}evel \textbf{R}e-ranking and Reconstruction), an unsupervised framework that decomposes retrieved documents into sentences, filters out irrelevant sentences, and reconstructs them again into coherent passages.We experimentally validate \textit{DSLR} on multiple open-domain QA datasets and the results demonstrate that \textit{DSLR} significantly enhances the RAG performance over conventional fixed-size passage.Furthermore, our \textit{DSLR} enhances performance in specific, yet realistic scenarios without the need for additional training, providing an effective and efficient solution for refining retrieved documents in RAG systems.
|
[
"Hwang, Taeho",
"Jeong, Soyeong",
"Cho, Sukmin",
"Han, SeungYoon",
"Park, Jong"
] |
{DSLR}: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation
|
knowledgenlp-1.6
|
Poster
|
2405.01122v1
|
https://aclanthology.org/2024.knowledgenlp-1.7.bib
|
@inproceedings{park-etal-2024-enhancing,
title = "Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning",
author = "Park, SeongIl and
Choi, Seungwoo and
Kim, Nahyun and
Lee, Jay-Yoon",
editor = "Yu, Wenhao and
Shi, Weijia and
Yasunaga, Michihiro and
Jiang, Meng and
Zhu, Chenguang and
Hajishirzi, Hannaneh and
Zettlemoyer, Luke and
Zhang, Zhihan",
booktitle = "Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowledgenlp-1.7",
pages = "93--102",
abstract = "Retrieval-Augmented Language Models (RALMs) have significantly improved performance in open-domain question answering (QA) by leveraging external knowledge. However, RALMs still struggle with unanswerable queries, where the retrieved contexts do not contain the correct answer, and with conflicting information, where different sources provide contradictory answers due to imperfect retrieval. This study introduces an in-context learning-based approach to enhance the reasoning capabilities of RALMs, making them more robust in imperfect retrieval scenarios. Our method incorporates Machine Reading Comprehension (MRC) demonstrations, referred to as $\textit{cases}$, to boost the model{'}s capabilities to identify unanswerabilities and conflicts among the retrieved contexts. Experiments on two open-domain QA datasets show that our approach increases accuracy in identifying unanswerable and conflicting scenarios without requiring additional fine-tuning. This work demonstrates that in-context learning can effectively enhance the robustness of RALMs in open-domain QA tasks.",
}
|
Retrieval-Augmented Language Models (RALMs) have significantly improved performance in open-domain question answering (QA) by leveraging external knowledge. However, RALMs still struggle with unanswerable queries, where the retrieved contexts do not contain the correct answer, and with conflicting information, where different sources provide contradictory answers due to imperfect retrieval. This study introduces an in-context learning-based approach to enhance the reasoning capabilities of RALMs, making them more robust in imperfect retrieval scenarios. Our method incorporates Machine Reading Comprehension (MRC) demonstrations, referred to as $\textit{cases}$, to boost the model{'}s capabilities to identify unanswerabilities and conflicts among the retrieved contexts. Experiments on two open-domain QA datasets show that our approach increases accuracy in identifying unanswerable and conflicting scenarios without requiring additional fine-tuning. This work demonstrates that in-context learning can effectively enhance the robustness of RALMs in open-domain QA tasks.
|
[
"Park, SeongIl",
"Choi, Seungwoo",
"Kim, Nahyun",
"Lee, Jay-Yoon"
] |
Enhancing Robustness of Retrieval-Augmented Language Models with In-Context Learning
|
knowledgenlp-1.7
|
Poster
|
2404.12352v1
|
https://aclanthology.org/2024.knowllm-1.1.bib
|
@inproceedings{suvarna-etal-2024-phonologybench,
title = "{P}honology{B}ench: Evaluating Phonological Skills of Large Language Models",
author = "Suvarna, Ashima and
Khandelwal, Harshita and
Peng, Nanyun",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.1",
pages = "1--14",
abstract = "Phonology, the study of speech{'}s structure and pronunciation rules, is a critical yet often overlooked component in Large Language Model (LLM) research. LLMs are widely used in various downstream applications that leverage phonology such as educational tools and poetry generation. Moreover, LLMs can potentially learn imperfect associations between orthographic and phonological forms from the training data. Thus, it is imperative to benchmark the phonological skills of LLMs. To this end, we present PhonologyBench, a novel benchmark consisting of three diagnostic tasks designed to explicitly test the phonological skills of LLMs in English: grapheme-to-phoneme conversion, syllable counting, and rhyme word generation. Despite having no access to speech data, LLMs showcased notable performance on the PhonologyBench tasks. However, we observe a significant gap of 17{\%} and 45{\%} on Rhyme Word Generation and Syllable counting, respectively, when compared to humans. Our findings underscore the importance of studying LLM performance on phonological tasks that inadvertently impact real-world applications. Furthermore, we encourage researchers to choose LLMs that perform well on the phonological task that is closely related to the downstream application since we find that no single model consistently outperforms the others on all the tasks.",
}
|
Phonology, the study of speech{'}s structure and pronunciation rules, is a critical yet often overlooked component in Large Language Model (LLM) research. LLMs are widely used in various downstream applications that leverage phonology such as educational tools and poetry generation. Moreover, LLMs can potentially learn imperfect associations between orthographic and phonological forms from the training data. Thus, it is imperative to benchmark the phonological skills of LLMs. To this end, we present PhonologyBench, a novel benchmark consisting of three diagnostic tasks designed to explicitly test the phonological skills of LLMs in English: grapheme-to-phoneme conversion, syllable counting, and rhyme word generation. Despite having no access to speech data, LLMs showcased notable performance on the PhonologyBench tasks. However, we observe a significant gap of 17{\%} and 45{\%} on Rhyme Word Generation and Syllable counting, respectively, when compared to humans. Our findings underscore the importance of studying LLM performance on phonological tasks that inadvertently impact real-world applications. Furthermore, we encourage researchers to choose LLMs that perform well on the phonological task that is closely related to the downstream application since we find that no single model consistently outperforms the others on all the tasks.
|
[
"Suvarna, Ashima",
"Kh",
"elwal, Harshita",
"Peng, Nanyun"
] |
{P}honology{B}ench: Evaluating Phonological Skills of Large Language Models
|
knowllm-1.1
|
Poster
|
2404.02456v2
|
https://aclanthology.org/2024.knowllm-1.2.bib
|
@inproceedings{balepur-rudinger-2024-large,
title = "Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?",
author = "Balepur, Nishant and
Rudinger, Rachel",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.2",
pages = "15--26",
abstract = "Recent work shows that large language models (LLMs) can answer multiple-choice questions using only the choices, but does this mean that MCQA leaderboard rankings of LLMs are largely influenced by abilities in choices-only settings? To answer this, we use a contrast set that probes if LLMs over-rely on choices-only shortcuts in MCQA. While previous works build contrast sets via expensive human annotations or model-generated data which can be biased, we employ graph mining to extract contrast sets from existing MCQA datasets. We use our method on UnifiedQA, a group of six commonsense reasoning datasets with high choices-only accuracy, to build an 820-question contrast set. After validating our contrast set, we test 12 LLMs, finding that these models do not exhibit reliance on choice-only shortcuts when given both the question and choices. Thus, despite the susceptibility of MCQA to high choices-only accuracy, we argue that LLMs are not obtaining high ranks on MCQA leaderboards solely due to their ability to exploit choices-only shortcuts.",
}
|
Recent work shows that large language models (LLMs) can answer multiple-choice questions using only the choices, but does this mean that MCQA leaderboard rankings of LLMs are largely influenced by abilities in choices-only settings? To answer this, we use a contrast set that probes if LLMs over-rely on choices-only shortcuts in MCQA. While previous works build contrast sets via expensive human annotations or model-generated data which can be biased, we employ graph mining to extract contrast sets from existing MCQA datasets. We use our method on UnifiedQA, a group of six commonsense reasoning datasets with high choices-only accuracy, to build an 820-question contrast set. After validating our contrast set, we test 12 LLMs, finding that these models do not exhibit reliance on choice-only shortcuts when given both the question and choices. Thus, despite the susceptibility of MCQA to high choices-only accuracy, we argue that LLMs are not obtaining high ranks on MCQA leaderboards solely due to their ability to exploit choices-only shortcuts.
|
[
"Balepur, Nishant",
"Rudinger, Rachel"
] |
Is Your Large Language Model Knowledgeable or a Choices-Only Cheater?
|
knowllm-1.2
|
Poster
|
1711.01100v1
|
https://aclanthology.org/2024.knowllm-1.3.bib
|
@inproceedings{yang-etal-2024-reassess,
title = "Reassess Summary Factual Inconsistency Detection with Large Language Model",
author = "Yang, Jiuding and
Liu, Hui and
Guo, Weidong and
Rao, Zhuwei and
Xu, Yu and
Niu, Di",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.3",
pages = "27--31",
abstract = "Ensuring factual consistency between the summary and the original document is paramount in summarization tasks. Consequently, considerable effort has been dedicated to detecting inconsistencies. With the advent of Large Language Models (LLMs), recent studies have begun to leverage their advanced language understanding capabilities for inconsistency detection. However, early attempts have shown that LLMs underperform traditional models due to their limited ability to follow instructions and the absence of an effective detection methodology. In this study, we reassess summary inconsistency detection with LLMs, comparing the performances of GPT-3.5 and GPT-4. To advance research in LLM-based inconsistency detection, we propose SIFiD (Summary Inconsistency Detection with Filtered Document) that identify key sentences within documents by either employing natural language inference or measuring semantic similarity between summaries and documents.",
}
|
Ensuring factual consistency between the summary and the original document is paramount in summarization tasks. Consequently, considerable effort has been dedicated to detecting inconsistencies. With the advent of Large Language Models (LLMs), recent studies have begun to leverage their advanced language understanding capabilities for inconsistency detection. However, early attempts have shown that LLMs underperform traditional models due to their limited ability to follow instructions and the absence of an effective detection methodology. In this study, we reassess summary inconsistency detection with LLMs, comparing the performances of GPT-3.5 and GPT-4. To advance research in LLM-based inconsistency detection, we propose SIFiD (Summary Inconsistency Detection with Filtered Document) that identify key sentences within documents by either employing natural language inference or measuring semantic similarity between summaries and documents.
|
[
"Yang, Jiuding",
"Liu, Hui",
"Guo, Weidong",
"Rao, Zhuwei",
"Xu, Yu",
"Niu, Di"
] |
Reassess Summary Factual Inconsistency Detection with Large Language Model
|
knowllm-1.3
|
Poster
|
2403.07557v1
|
https://aclanthology.org/2024.knowllm-1.4.bib
|
@inproceedings{liu-etal-2024-beyond-text,
title = "Beyond Text: Unveiling Multimodal Proficiency of Large Language Models with {M}ulti{API} Benchmark",
author = "Liu, Xiao and
Lin, Jianfeng and
Zhang, Jiawei",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.4",
pages = "32--44",
abstract = "The proliferation of Large Language Models like ChatGPT has significantly advanced language understanding and generation, impacting a broad spectrum of applications. However, these models predominantly excel in text-based tasks, overlooking the complexity of real-world multimodal information. This study introduces \textbf{MultiAPI}, a pioneering comprehensive large-scale API benchmark dataset aimed at expanding LLMs{'} proficiency in multimodal contexts. Developed collaboratively through ChatGPT, \textbf{MultiAPI} consists of 187 diverse API calls and 1,799 contextual prompts, offering a unique platform evaluation of tool-augmented LLMs handling multimodal tasks. Through comprehensive experiments, our findings reveal that while LLMs demonstrate proficiency in API call decision-making, they face challenges in domain identification, function selection, and argument generation. What{'}s more, we surprisingly notice that auxiliary context can actually impair the performance. An in-depth error analysis paves the way for a new paradigm to address these challenges, suggesting a potential direction for future LLM research.",
}
|
The proliferation of Large Language Models like ChatGPT has significantly advanced language understanding and generation, impacting a broad spectrum of applications. However, these models predominantly excel in text-based tasks, overlooking the complexity of real-world multimodal information. This study introduces \textbf{MultiAPI}, a pioneering comprehensive large-scale API benchmark dataset aimed at expanding LLMs{'} proficiency in multimodal contexts. Developed collaboratively through ChatGPT, \textbf{MultiAPI} consists of 187 diverse API calls and 1,799 contextual prompts, offering a unique platform evaluation of tool-augmented LLMs handling multimodal tasks. Through comprehensive experiments, our findings reveal that while LLMs demonstrate proficiency in API call decision-making, they face challenges in domain identification, function selection, and argument generation. What{'}s more, we surprisingly notice that auxiliary context can actually impair the performance. An in-depth error analysis paves the way for a new paradigm to address these challenges, suggesting a potential direction for future LLM research.
|
[
"Liu, Xiao",
"Lin, Jianfeng",
"Zhang, Jiawei"
] |
Beyond Text: Unveiling Multimodal Proficiency of Large Language Models with {M}ulti{API} Benchmark
|
knowllm-1.4
|
Poster
|
2311.13053v1
|
https://aclanthology.org/2024.knowllm-1.5.bib
|
@inproceedings{chen-etal-2024-retrieval,
title = "Retrieval-Augmented Knowledge Integration into Language Models: A Survey",
author = {Chen, Yuxuan and
R{\"o}der, Daniel and
Erker, Justus-Jonas and
Hennig, Leonhard and
Thomas, Philippe and
M{\"o}ller, Sebastian and
Roller, Roland},
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.5",
pages = "45--63",
abstract = "This survey analyses how external knowledge can be integrated into language models in the context of retrieval-augmentation.The main goal of this work is to give an overview of: (1) Which external knowledge can be augmented? (2) Given a knowledge source, how to retrieve from it and then integrate the retrieved knowledge? To achieve this, we define and give a mathematical formulation of retrieval-augmented knowledge integration (RAKI). We discuss retrieval and integration techniques separately in detail, for each of the following knowledge formats: knowledge graph, tabular and natural language.",
}
|
This survey analyses how external knowledge can be integrated into language models in the context of retrieval-augmentation.The main goal of this work is to give an overview of: (1) Which external knowledge can be augmented? (2) Given a knowledge source, how to retrieve from it and then integrate the retrieved knowledge? To achieve this, we define and give a mathematical formulation of retrieval-augmented knowledge integration (RAKI). We discuss retrieval and integration techniques separately in detail, for each of the following knowledge formats: knowledge graph, tabular and natural language.
|
[
"Chen, Yuxuan",
"R{\\\"o}der, Daniel",
"Erker, Justus-Jonas",
"Hennig, Leonhard",
"Thomas, Philippe",
"M{\\\"o}ller, Sebastian",
"Roller, Rol",
""
] |
Retrieval-Augmented Knowledge Integration into Language Models: A Survey
|
knowllm-1.5
|
Poster
|
2309.16459v1
|
https://aclanthology.org/2024.knowllm-1.6.bib
|
@inproceedings{lu-etal-2024-clinicalrag,
title = "{C}linical{RAG}: Enhancing Clinical Decision Support through Heterogeneous Knowledge Retrieval",
author = "Lu, Yuxing and
Zhao, Xukai and
Wang, Jinzhuo",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.6",
pages = "64--68",
abstract = "Large Language Models (LLMs) have revolutionized text generation across diverse domains, showcasing an ability to mimic human-like text with remarkable accuracy. Yet, these models frequently encounter a significant hurdle: producing hallucinations, a flaw particularly detrimental in the healthcare domain where precision is crucial. In this paper, we introduce ClinicalRAG, a novel multi-agent pipeline to rectify this issue by incorporating heterogeneous medical knowledge{---}both structured and unstructured{---}into LLMs to bolster diagnosis accuracy. ClinicalRAG can extract related medical entities from user inputs and dynamically integrate relevant medical knowledge during the text generation process. Comparative analyses reveal that ClinicalRAG significantly outperforms knowledge-deficient methods, offering enhanced reliability in clinical decision support. This advancement marks a pivotal proof-of-concept step towards mitigating misinformation risks in healthcare applications of LLMs.",
}
|
Large Language Models (LLMs) have revolutionized text generation across diverse domains, showcasing an ability to mimic human-like text with remarkable accuracy. Yet, these models frequently encounter a significant hurdle: producing hallucinations, a flaw particularly detrimental in the healthcare domain where precision is crucial. In this paper, we introduce ClinicalRAG, a novel multi-agent pipeline to rectify this issue by incorporating heterogeneous medical knowledge{---}both structured and unstructured{---}into LLMs to bolster diagnosis accuracy. ClinicalRAG can extract related medical entities from user inputs and dynamically integrate relevant medical knowledge during the text generation process. Comparative analyses reveal that ClinicalRAG significantly outperforms knowledge-deficient methods, offering enhanced reliability in clinical decision support. This advancement marks a pivotal proof-of-concept step towards mitigating misinformation risks in healthcare applications of LLMs.
|
[
"Lu, Yuxing",
"Zhao, Xukai",
"Wang, Jinzhuo"
] |
{C}linical{RAG}: Enhancing Clinical Decision Support through Heterogeneous Knowledge Retrieval
|
knowllm-1.6
|
Poster
|
1609.01592v1
|
https://aclanthology.org/2024.knowllm-1.7.bib
|
@inproceedings{liu-etal-2024-modeling,
title = "Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with {LLM}s",
author = "Liu, Ye and
Meng, Rui and
Bhat, Meghana Moorthy and
Joty, Shafiq and
Xiong, Caiming and
Zhou, Yingbo and
Yavuz, Semih",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.7",
pages = "69--82",
abstract = "The integration of retrieved passages and large language models (LLMs), such as ChatGPTs, has significantly contributed to improving open-domain question answering. However, there is still a lack of exploration regarding the optimal approach for incorporating retrieved passages into the answer generation process. This paper aims to fill this gap by investigating different methods of combining retrieved passages with LLMs to enhance answer generation. We begin by examining the limitations of a commonly-used concatenation approach. Surprisingly, this approach often results in generating {``}unknown{''} outputs, even when the correct document is among the top-$k$ retrieved passages. To address this issue, we explore four alternative strategies for integrating the retrieved passages with the LLMs. These strategies include two single-round methods that utilize chain-of-thought reasoning and two multi-round strategies that incorporate feedback loops. Through comprehensive analyses and experiments, we provide insightful observations on how to effectively leverage retrieved passages to enhance the answer generation capability of LLMs. On three open-domain question answering datesets, NQ, TriviaQA and SQuAD, our multi-round approaches outperform traditional concatenation approach, achieving over a 10{\%} improvement in answer EM.",
}
|
The integration of retrieved passages and large language models (LLMs), such as ChatGPTs, has significantly contributed to improving open-domain question answering. However, there is still a lack of exploration regarding the optimal approach for incorporating retrieved passages into the answer generation process. This paper aims to fill this gap by investigating different methods of combining retrieved passages with LLMs to enhance answer generation. We begin by examining the limitations of a commonly-used concatenation approach. Surprisingly, this approach often results in generating {``}unknown{''} outputs, even when the correct document is among the top-$k$ retrieved passages. To address this issue, we explore four alternative strategies for integrating the retrieved passages with the LLMs. These strategies include two single-round methods that utilize chain-of-thought reasoning and two multi-round strategies that incorporate feedback loops. Through comprehensive analyses and experiments, we provide insightful observations on how to effectively leverage retrieved passages to enhance the answer generation capability of LLMs. On three open-domain question answering datesets, NQ, TriviaQA and SQuAD, our multi-round approaches outperform traditional concatenation approach, achieving over a 10{\%} improvement in answer EM.
|
[
"Liu, Ye",
"Meng, Rui",
"Bhat, Meghana Moorthy",
"Joty, Shafiq",
"Xiong, Caiming",
"Zhou, Yingbo",
"Yavuz, Semih"
] |
Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with {LLM}s
|
knowllm-1.7
|
Poster
|
2308.12574v2
|
https://aclanthology.org/2024.knowllm-1.8.bib
|
@inproceedings{das-etal-2024-acknowledge,
title = "{A}c{K}nowledge: Acquired Knowledge Representation by Small Language Model Without Pre-training",
author = "Das, Sourav and
Chatterji, Sanjay and
Mukherjee, Imon",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.8",
pages = "83--95",
abstract = "Large language models (LLMs) are pre-trained on enormous amounts of text data and show acclaimed success in knowledge representation. However, there are two bottlenecks with this approach. (1) Pre-training data cannot be regularly updated once the models are deployed, and it is not very fruitful if the model cannot represent updated knowledge. (2) The consistently increasing size and computational resources make it difficult for non-commercial and individual researchers to fine-tune and scale these language models. Major LLMs with external knowledge are also proprietary. In this paper, we propose AcKnowledge, a framework wrapped around a small, non-pre-trained language model for an open-domain question-answering (QA) experiment. AcKnowledge learns relevant knowledge from the internet via meta-learning based on user questions, and re-learns from user feedback if knowledge is misrepresented. Our efficient knowledge representation framework avoids pre-training overhead while enabling updated information. Benchmarking shows competitive performance against similarly sized state-of-the-art (SoTA) LLMs on gold standard QA datasets, demonstrating the potential of integrating internet search and user feedback for improved performance and generalizability.",
}
|
Large language models (LLMs) are pre-trained on enormous amounts of text data and show acclaimed success in knowledge representation. However, there are two bottlenecks with this approach. (1) Pre-training data cannot be regularly updated once the models are deployed, and it is not very fruitful if the model cannot represent updated knowledge. (2) The consistently increasing size and computational resources make it difficult for non-commercial and individual researchers to fine-tune and scale these language models. Major LLMs with external knowledge are also proprietary. In this paper, we propose AcKnowledge, a framework wrapped around a small, non-pre-trained language model for an open-domain question-answering (QA) experiment. AcKnowledge learns relevant knowledge from the internet via meta-learning based on user questions, and re-learns from user feedback if knowledge is misrepresented. Our efficient knowledge representation framework avoids pre-training overhead while enabling updated information. Benchmarking shows competitive performance against similarly sized state-of-the-art (SoTA) LLMs on gold standard QA datasets, demonstrating the potential of integrating internet search and user feedback for improved performance and generalizability.
|
[
"Das, Sourav",
"Chatterji, Sanjay",
"Mukherjee, Imon"
] |
{A}c{K}nowledge: Acquired Knowledge Representation by Small Language Model Without Pre-training
|
knowllm-1.8
|
Poster
|
2301.08577v1
|
https://aclanthology.org/2024.knowllm-1.9.bib
|
@inproceedings{hoffbauer-etal-2024-knowledge,
title = "Knowledge Acquisition through Continued Pretraining is Difficult: A Case Study on r/{A}sk{H}istorians",
author = "Hoffbauer, Jan and
Sawicki, Sylwester and
Ulrich, Marc and
Buz, Tolga and
Dobler, Konstantin and
Schneider, Moritz and
De Melo, Gerard",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.9",
pages = "96--108",
abstract = "Powerful LLMs like ChatGPT are adopted rapidly for a wide array of tasks, but their limitations in domain-specific areas become apparent, particularly when prompted to recite facts. This is critical especially for knowledge workers, who are adopting LLM-based tools rapidly.While there are various techniques that can help ingest knowledge into LLMs such as instruction tuning and alignment, most have disadvantages. We examine the impact of prominent training techniques on LLMs{'} knowledge accuracy using a knowledge-dense dataset that we curate from r/AskHistorians, a rich source of historical knowledge. We evaluate the impact of different models sizes from 1.3B to 7B parameters and other factors such as LoRA adapters, quantization, overfitting, and the inclusion of Reddit data in pretraining.In addition, we measure linguistic metrics and human and LLM-based preference. Our results suggest that pretraining and model size have a much stronger effect on knowledge accuracy than continued pretraining {--} unless the model is overfit to the tested knowledge.Fine-tuning on our Reddit dataset introduces less complex, but slightly more toxic language. Our study explores the challenges of injecting domain-specific datasets into LLMs and has implications for practitioners, e.g., when LLMs are to be fine-tuned with a company{'}s datasets.",
}
|
Powerful LLMs like ChatGPT are adopted rapidly for a wide array of tasks, but their limitations in domain-specific areas become apparent, particularly when prompted to recite facts. This is critical especially for knowledge workers, who are adopting LLM-based tools rapidly.While there are various techniques that can help ingest knowledge into LLMs such as instruction tuning and alignment, most have disadvantages. We examine the impact of prominent training techniques on LLMs{'} knowledge accuracy using a knowledge-dense dataset that we curate from r/AskHistorians, a rich source of historical knowledge. We evaluate the impact of different models sizes from 1.3B to 7B parameters and other factors such as LoRA adapters, quantization, overfitting, and the inclusion of Reddit data in pretraining.In addition, we measure linguistic metrics and human and LLM-based preference. Our results suggest that pretraining and model size have a much stronger effect on knowledge accuracy than continued pretraining {--} unless the model is overfit to the tested knowledge.Fine-tuning on our Reddit dataset introduces less complex, but slightly more toxic language. Our study explores the challenges of injecting domain-specific datasets into LLMs and has implications for practitioners, e.g., when LLMs are to be fine-tuned with a company{'}s datasets.
|
[
"Hoffbauer, Jan",
"Sawicki, Sylwester",
"Ulrich, Marc",
"Buz, Tolga",
"Dobler, Konstantin",
"Schneider, Moritz",
"De Melo, Gerard"
] |
Knowledge Acquisition through Continued Pretraining is Difficult: A Case Study on r/{A}sk{H}istorians
|
knowllm-1.9
|
Poster
|
2407.03477v1
|
https://aclanthology.org/2024.knowllm-1.10.bib
|
@inproceedings{lyu-etal-2024-beyond,
title = "Beyond Probabilities: Unveiling the Misalignment in Evaluating Large Language Models",
author = "Lyu, Chenyang and
Wu, Minghao and
Aji, Alham",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.10",
pages = "109--131",
abstract = "Large Language Models (LLMs) have demonstrated remarkable capabilities across various applications, fundamentally reshaping the landscape of natural language processing (NLP) research. However, recent evaluation frameworks often rely on the output probabilities of LLMs for predictions, primarily due to computational constraints, diverging from real-world LLM usage scenarios. While widely employed, the efficacy of these probability-based evaluation strategies remains an open research question. This study aims to scrutinize the validity of such probability-based evaluation methods within the context of using LLMs for Multiple Choice Questions (MCQs), highlighting their inherent limitations. Our empirical investigation reveals that the prevalent probability-based evaluation method inadequately aligns with generation-based prediction. Furthermore, current evaluation frameworks typically assess LLMs through predictive tasks based on output probabilities rather than directly generating responses, owing to computational limitations. We illustrate that these probability-based approaches do not effectively correspond with generative predictions. The outcomes of our study can enhance the understanding of LLM evaluation methodologies and provide insights for future research in this domain.",
}
|
Large Language Models (LLMs) have demonstrated remarkable capabilities across various applications, fundamentally reshaping the landscape of natural language processing (NLP) research. However, recent evaluation frameworks often rely on the output probabilities of LLMs for predictions, primarily due to computational constraints, diverging from real-world LLM usage scenarios. While widely employed, the efficacy of these probability-based evaluation strategies remains an open research question. This study aims to scrutinize the validity of such probability-based evaluation methods within the context of using LLMs for Multiple Choice Questions (MCQs), highlighting their inherent limitations. Our empirical investigation reveals that the prevalent probability-based evaluation method inadequately aligns with generation-based prediction. Furthermore, current evaluation frameworks typically assess LLMs through predictive tasks based on output probabilities rather than directly generating responses, owing to computational limitations. We illustrate that these probability-based approaches do not effectively correspond with generative predictions. The outcomes of our study can enhance the understanding of LLM evaluation methodologies and provide insights for future research in this domain.
|
[
"Lyu, Chenyang",
"Wu, Minghao",
"Aji, Alham"
] |
Beyond Probabilities: Unveiling the Misalignment in Evaluating Large Language Models
|
knowllm-1.10
|
Poster
|
2402.13887v2
|
https://aclanthology.org/2024.knowllm-1.11.bib
|
@inproceedings{gao-etal-2024-promptre,
title = "{P}rompt{RE}: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming",
author = "Gao, Chufan and
Fan, Xulin and
Sun, Jimeng and
Wang, Xuan",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.11",
pages = "132--145",
abstract = "Relation extraction aims to classify the relationships between two entities into pre-defined categories. While previous research has mainly focused on sentence-level relation extraction, recent studies have expanded the scope to document-level relation extraction. Traditional relation extraction methods heavily rely on human-annotated training data, which is time-consuming and labor-intensive. To mitigate the need for manual annotation, recent weakly-supervised approaches have been developed for sentence-level relation extraction while limited work has been done on document-level relation extraction. Weakly-supervised document-level relation extraction faces significant challenges due to an imbalanced number {``}no relation{''} instances and the failure of directly probing pretrained large language models for document relation extraction. To address these challenges, we propose PromptRE, a novel weakly-supervised document-level relation extraction method that combines prompting-based techniques with data programming. Furthermore, PromptRE incorporates the label distribution and entity types as prior knowledge to improve the performance. By leveraging the strengths of both prompting and data programming, PromptRE achieves improved performance in relation classification and effectively handles the {``}no relation{''} problem. Experimental results on ReDocRED, a benchmark dataset for document-level relation extraction, demonstrate the superiority of PromptRE over baseline approaches.",
}
|
Relation extraction aims to classify the relationships between two entities into pre-defined categories. While previous research has mainly focused on sentence-level relation extraction, recent studies have expanded the scope to document-level relation extraction. Traditional relation extraction methods heavily rely on human-annotated training data, which is time-consuming and labor-intensive. To mitigate the need for manual annotation, recent weakly-supervised approaches have been developed for sentence-level relation extraction while limited work has been done on document-level relation extraction. Weakly-supervised document-level relation extraction faces significant challenges due to an imbalanced number {``}no relation{''} instances and the failure of directly probing pretrained large language models for document relation extraction. To address these challenges, we propose PromptRE, a novel weakly-supervised document-level relation extraction method that combines prompting-based techniques with data programming. Furthermore, PromptRE incorporates the label distribution and entity types as prior knowledge to improve the performance. By leveraging the strengths of both prompting and data programming, PromptRE achieves improved performance in relation classification and effectively handles the {``}no relation{''} problem. Experimental results on ReDocRED, a benchmark dataset for document-level relation extraction, demonstrate the superiority of PromptRE over baseline approaches.
|
[
"Gao, Chufan",
"Fan, Xulin",
"Sun, Jimeng",
"Wang, Xuan"
] |
{P}rompt{RE}: Weakly-Supervised Document-Level Relation Extraction via Prompting-Based Data Programming
|
knowllm-1.11
|
Poster
|
2310.09265v1
|
https://aclanthology.org/2024.knowllm-1.12.bib
|
@inproceedings{chu-etal-2024-patent,
title = "Patent Response System Optimised for Faithfulness: Procedural Knowledge Embodiment with Knowledge Graph and Retrieval Augmented Generation",
author = "Chu, Jung-Mei and
Lo, Hao-Cheng and
Hsiang, Jieh and
Cho, Chun-Chieh",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.12",
pages = "146--155",
abstract = "A successful response to Office Action is crucial for an invention to obtain a patent. While previous attempts have applied generalised LLMs, such as GPT-4, in the response process, there remains significant room for improvement in generating faithful, unbiased, and practically valuable responses. To address this issue, we propose the Patent Response System Optimised for Faithfulness (PRO). PRO explicitly incorporates procedural knowledge used by patent agents during drafting arguments in response. This framework comprises several key components: (1) Our proposed PRLLM is a LLM tailored for patent responses, designed to have comprehensive patent domain-specific knowledge. (2) Our proposed PPNet encodes legal interpretations and relationships between technical components from judicial sources through a knowledge graph. (3) The augmented generation processes retrieve relevant information from both the patent text and PPNet to augment the PRLLM{'}s input and generate faithful responses. Results show that PRO significantly reduces unfaithfulness across six error types compared to several settings. For instance, PRO outperforms GPT-4 by an average of 39{\%} in terms of faithfulness. This demonstrates the effectiveness of our domain-specific approach in improving the quality of automated patent responses.",
}
|
A successful response to Office Action is crucial for an invention to obtain a patent. While previous attempts have applied generalised LLMs, such as GPT-4, in the response process, there remains significant room for improvement in generating faithful, unbiased, and practically valuable responses. To address this issue, we propose the Patent Response System Optimised for Faithfulness (PRO). PRO explicitly incorporates procedural knowledge used by patent agents during drafting arguments in response. This framework comprises several key components: (1) Our proposed PRLLM is a LLM tailored for patent responses, designed to have comprehensive patent domain-specific knowledge. (2) Our proposed PPNet encodes legal interpretations and relationships between technical components from judicial sources through a knowledge graph. (3) The augmented generation processes retrieve relevant information from both the patent text and PPNet to augment the PRLLM{'}s input and generate faithful responses. Results show that PRO significantly reduces unfaithfulness across six error types compared to several settings. For instance, PRO outperforms GPT-4 by an average of 39{\%} in terms of faithfulness. This demonstrates the effectiveness of our domain-specific approach in improving the quality of automated patent responses.
|
[
"Chu, Jung-Mei",
"Lo, Hao-Cheng",
"Hsiang, Jieh",
"Cho, Chun-Chieh"
] |
Patent Response System Optimised for Faithfulness: Procedural Knowledge Embodiment with Knowledge Graph and Retrieval Augmented Generation
|
knowllm-1.12
|
Poster
|
2211.01976v1
|
https://aclanthology.org/2024.knowllm-1.13.bib
|
@inproceedings{kim-etal-2024-safe,
title = "Safe-Embed: Unveiling the Safety-Critical Knowledge of Sentence Encoders",
author = "Kim, Jinseok and
Jung, Jaewon and
Kim, Sangyeop and
Park, Sohhyung and
Cho, Sungzoon",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.13",
pages = "156--170",
abstract = "Despite the impressive capabilities of Large Language Models (LLMs) in various tasks, their vulnerability to unsafe prompts remains a critical issue. These prompts can lead LLMs to generate responses on illegal or sensitive topics, posing a significant threat to their safe and ethical use. Existing approaches address this issue using classification models, divided into LLM-based and API-based methods. LLM based models demand substantial resources and large datasets, whereas API-based models are cost-effective but might overlook linguistic nuances. With the increasing complexity of unsafe prompts, similarity search-based techniques that identify specific features of unsafe content provide a more robust and effective solution to this evolving problem. This paper investigates the potential of sentence encoders to distinguish safe from unsafe content. We introduce new pairwise datasets and the Cate021 gorical Purity (CP) metric to measure this capability. Our findings reveal both the effectiveness and limitations of existing sentence encoders, proposing directions to improve sentence encoders to operate as robust safety detectors.",
}
|
Despite the impressive capabilities of Large Language Models (LLMs) in various tasks, their vulnerability to unsafe prompts remains a critical issue. These prompts can lead LLMs to generate responses on illegal or sensitive topics, posing a significant threat to their safe and ethical use. Existing approaches address this issue using classification models, divided into LLM-based and API-based methods. LLM based models demand substantial resources and large datasets, whereas API-based models are cost-effective but might overlook linguistic nuances. With the increasing complexity of unsafe prompts, similarity search-based techniques that identify specific features of unsafe content provide a more robust and effective solution to this evolving problem. This paper investigates the potential of sentence encoders to distinguish safe from unsafe content. We introduce new pairwise datasets and the Cate021 gorical Purity (CP) metric to measure this capability. Our findings reveal both the effectiveness and limitations of existing sentence encoders, proposing directions to improve sentence encoders to operate as robust safety detectors.
|
[
"Kim, Jinseok",
"Jung, Jaewon",
"Kim, Sangyeop",
"Park, Sohhyung",
"Cho, Sungzoon"
] |
Safe-Embed: Unveiling the Safety-Critical Knowledge of Sentence Encoders
|
knowllm-1.13
|
Poster
|
1609.07075v1
|
https://aclanthology.org/2024.knowllm-1.14.bib
|
@inproceedings{zhao-etal-2024-measuring,
title = "Measuring the Inconsistency of Large Language Models in Preferential Ranking",
author = "Zhao, Xiutian and
Wang, Ke and
Peng, Wei",
editor = "Li, Sha and
Li, Manling and
Zhang, Michael JQ and
Choi, Eunsol and
Geva, Mor and
Hase, Peter and
Ji, Heng",
booktitle = "Proceedings of the 1st Workshop on Towards Knowledgeable Language Models (KnowLLM 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.knowllm-1.14",
pages = "171--176",
abstract = "Despite large language models{'} (LLMs{'}) recent advancements, their bias and hallucination issues persist, and their ability to offer consistent and preferential rankings remains underexplored. This study investigates the capacity of LLMs to provide consistent ordinal preferences, a crucial aspect in scenarios lacking absolute answers. We introduce a formalization of consistency based on order theory, outlining criteria such as transitivity, asymmetry, reversibility, and independence from irrelevant alternatives. Our diagnostic experiments on selected state-of-the-art LLMs reveal their inability to meet these criteria, indicating a strong positional bias and poor transitivity, with preferences easily swayed by irrelevant alternatives. These findings highlight a significant inconsistency in LLM-generated preferential rankings, underscoring the need for further research to address these limitations.",
}
|
Despite large language models{'} (LLMs{'}) recent advancements, their bias and hallucination issues persist, and their ability to offer consistent and preferential rankings remains underexplored. This study investigates the capacity of LLMs to provide consistent ordinal preferences, a crucial aspect in scenarios lacking absolute answers. We introduce a formalization of consistency based on order theory, outlining criteria such as transitivity, asymmetry, reversibility, and independence from irrelevant alternatives. Our diagnostic experiments on selected state-of-the-art LLMs reveal their inability to meet these criteria, indicating a strong positional bias and poor transitivity, with preferences easily swayed by irrelevant alternatives. These findings highlight a significant inconsistency in LLM-generated preferential rankings, underscoring the need for further research to address these limitations.
|
[
"Zhao, Xiutian",
"Wang, Ke",
"Peng, Wei"
] |
Measuring the Inconsistency of Large Language Models in Preferential Ranking
|
knowllm-1.14
|
Poster
|
2406.00231v1
|
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