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lm-evaluation/lm_eval/tasks/glue/README.md

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+# GLUE
+**NOTE**: GLUE benchmark tasks do not provide publicly accessible labels for their test sets, so we default to the validation sets for all sub-tasks.
+
+### Paper
+
+Title: `GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding`
+
+Abstract: https://openreview.net/pdf?id=rJ4km2R5t7
+
+The General Language Understanding Evaluation (GLUE) benchmark is a collection of
+resources for training, evaluating, and analyzing natural language understanding
+systems. GLUE consists of:
+- A benchmark of nine sentence- or sentence-pair language understanding tasks built
+on established existing datasets and selected to cover a diverse range of dataset
+sizes, text genres, and degrees of difficulty, and
+- A diagnostic dataset designed to evaluate and analyze model performance with
+respect to a wide range of linguistic phenomena found in natural language.
+
+Homepage: https://gluebenchmark.com/
+
+### Citation
+
+```
+@inproceedings{wang-etal-2018-glue,
+    title = "{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding",
+    author = "Wang, Alex  and
+      Singh, Amanpreet  and
+      Michael, Julian  and
+      Hill, Felix  and
+      Levy, Omer  and
+      Bowman, Samuel",
+    booktitle = "Proceedings of the 2018 {EMNLP} Workshop {B}lackbox{NLP}: Analyzing and Interpreting Neural Networks for {NLP}",
+    month = nov,
+    year = "2018",
+    address = "Brussels, Belgium",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W18-5446",
+    doi = "10.18653/v1/W18-5446",
+    pages = "353--355",
+    abstract = "Human ability to understand language is \textit{general, flexible, and robust}. In contrast, most NLU models above the word level are designed for a specific task and struggle with out-of-domain data. If we aspire to develop models with understanding beyond the detection of superficial correspondences between inputs and outputs, then it is critical to develop a unified model that can execute a range of linguistic tasks across different domains. To facilitate research in this direction, we present the General Language Understanding Evaluation (GLUE, gluebenchmark.com): a benchmark of nine diverse NLU tasks, an auxiliary dataset for probing models for understanding of specific linguistic phenomena, and an online platform for evaluating and comparing models. For some benchmark tasks, training data is plentiful, but for others it is limited or does not match the genre of the test set. GLUE thus favors models that can represent linguistic knowledge in a way that facilitates sample-efficient learning and effective knowledge-transfer across tasks. While none of the datasets in GLUE were created from scratch for the benchmark, four of them feature privately-held test data, which is used to ensure that the benchmark is used fairly. We evaluate baselines that use ELMo (Peters et al., 2018), a powerful transfer learning technique, as well as state-of-the-art sentence representation models. The best models still achieve fairly low absolute scores. Analysis with our diagnostic dataset yields similarly weak performance over all phenomena tested, with some exceptions.",
+}
+```
+
+### Groups and Tasks
+
+#### Groups
+
+* `glue`: Run all Glue subtasks.
+
+#### Tasks
+
+* `cola`
+* `mnli`
+* `mrpc`
+* `qnli`
+* `qqp`
+* `rte`
+* `sst`
+* `wnli`
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [ ] Is the task an existing benchmark in the literature?
+  * [ ] Have you referenced the original paper that introduced the task?
+  * [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
+
+
+If other tasks on this dataset are already supported:
+* [ ] Is the "Main" variant of this task clearly denoted?
+* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?

lm-evaluation/lm_eval/tasks/glue/cola/default.yaml

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+group: glue
+task: cola
+dataset_path: glue
+dataset_name: cola
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "{{sentence}}\nQuestion: Does this sentence make sense?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["no", "yes"]
+should_decontaminate: true
+doc_to_decontamination_query: sentence
+metric_list:
+  - metric: mcc
+metadata:
+  version: 1.0

lm-evaluation/lm_eval/tasks/glue/mnli/default.yaml

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+group: glue
+task: mnli
+dataset_path: glue
+dataset_name: mnli
+output_type: multiple_choice
+training_split: train
+validation_split: validation_matched
+doc_to_text: !function utils.doc_to_text
+doc_to_target: label
+doc_to_choice: ["True", "Neither", "False"]
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0

lm-evaluation/lm_eval/tasks/glue/mnli/mismatch.yaml

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+include: default.yaml
+task: mnli_mismatch
+validation_split: validation_mismatched

lm-evaluation/lm_eval/tasks/glue/mnli/utils.py

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+def doc_to_text(doc) -> str:
+    return "{}\nQuestion: {} True, False or Neither?\nAnswer:".format(
+        doc["premise"],
+        doc["hypothesis"].strip()
+        + ("" if doc["hypothesis"].strip().endswith(".") else "."),
+    )

lm-evaluation/lm_eval/tasks/glue/mrpc/default.yaml

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+group: glue
+task: mrpc
+dataset_path: glue
+dataset_name: mrpc
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "Sentence 1: {{sentence1}}\nSentence 2: {{sentence2}}\nQuestion: Do both sentences mean the same thing?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["no", "yes"]
+metric_list:
+  - metric: acc
+  - metric: f1
+metadata:
+  version: 1.0

lm-evaluation/lm_eval/tasks/glue/qqp/default.yaml

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+group: glue
+task: qqp
+dataset_path: glue
+dataset_name: qqp
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "Question 1: {{question1}}\nQuestion 2: {{question2}}\nQuestion: Do both questions ask the same thing?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["no", "yes"]
+metric_list:
+  - metric: acc
+  - metric: f1
+metadata:
+  version: 2.0

lm-evaluation/lm_eval/tasks/glue/rte/default.yaml

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+group: glue
+task: rte
+dataset_path: glue
+dataset_name: rte
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "{{sentence1}}\nQuestion: {{sentence2}} True or False?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["True", "False"]
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0

lm-evaluation/lm_eval/tasks/glue/sst2/default.yaml

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+group: glue
+task: sst2
+dataset_path: glue
+dataset_name: sst2
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "{{sentence}}\nQuestion: Is this sentence positive or negative?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["negative", "positive"]
+metric_list:
+  - metric: acc
+metadata:
+  version: 1.0

lm-evaluation/lm_eval/tasks/glue/wnli/default.yaml

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+group: glue
+task: wnli
+dataset_path: glue
+dataset_name: wnli
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+doc_to_text: "{{sentence1}}\nQuestion: {{sentence2}} True or False?\nAnswer:"
+doc_to_target: label
+doc_to_choice: ["False", "True"]
+metric_list:
+  - metric: acc
+metadata:
+  version: 2.0

lm-evaluation/lm_eval/tasks/kmmlu/README.md

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+# k_mmlu
+
+### Paper
+
+Title: `KMMLU : Measuring Massive Multitask Language Understanding in Korean`
+
+Abstract: `We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. Unlike previous Korean benchmarks that are translated from existing English benchmarks, KMMLU is collected from original Korean exams, capturing linguistic and cultural aspects of the Korean language. We test 26 publicly available and proprietary LLMs, identifying significant room for improvement. The best publicly available model achieves 50.54% on KMMLU, far below the average human performance of 62.6%. This model was primarily trained for English and Chinese, not Korean. Current LLMs tailored to Korean, such as Polyglot-Ko, perform far worse. Surprisingly, even the most capable proprietary LLMs, e.g., GPT-4 and HyperCLOVA X, achieve 59.95% and 53.40%, respectively. This suggests that further work is needed to improve Korean LLMs, and KMMLU offers the right tool to track this progress. We make our dataset publicly available on the Hugging Face Hub and integrate the benchmark into EleutherAI's Language Model Evaluation Harness.`
+
+Note: lm-eval-harness is using the micro average as the default. To replicate the test results in the paper, take the macro average for the scores evaluated with lm-eval-harness
+
+Homepage: https://huggingface.co/datasets/HAERAE-HUB/KMMLU
+
+### Citation
+
+@article{son2024kmmlu,
+      title={KMMLU: Measuring Massive Multitask Language Understanding in Korean},
+      author={Guijin Son and Hanwool Lee and Sungdong Kim and Seungone Kim and Niklas Muennighoff and Taekyoon Choi and Cheonbok Park and Kang Min Yoo and Stella Biderman},
+      journal={arXiv preprint arXiv:2402.11548},
+      year={2024}
+}
+
+### Groups and Tasks
+
+#### Groups
+
+* `kmmlu`: 'All 45 subjects of the KMMLU dataset, evaluated following the methodology in MMLU's original implementation'
+* `kmmlu_direct`: 'kmmlu_direct solves questions using a straightforward *generative* multiple-choice question-answering approach'
+* `kmmlu_hard`: 'kmmlu_hard comprises difficult questions that at least one proprietary model failed to answer correctly using log-likelihood approach'
+* `kmmlu_hard_direct`:  'kmmlu_hard_direct solves questions of kmmlu_hard using direct(generative) approach'
+* `kmmlu_hard_cot`: 'kmmlu_hard_cot includes 5-shot of exemplars for chain-of-thought approach'
+
+#### Tasks
+
+The following tasks evaluate subjects in the KMMLU dataset
+- `kmmlu_direct_{subject_english}`
+
+The following tasks evaluate subjects in the KMMLU-Hard dataset
+- `kmmlu_hard_{subject_english}`
+- `kmmlu_hard_cot_{subject_english}`
+- `kmmlu_hard_direct_{subject_english}`
+
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+* [x] Is the task an existing benchmark in the literature?
+  * [x] Have you referenced the original paper that introduced the task?
+  * [x] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
+
+
+If other tasks on this dataset are already supported:
+* [ ] Is the "Main" variant of this task clearly denoted?
+* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?

lm-evaluation/lm_eval/tasks/kmmlu/direct/_direct_kmmlu_yaml

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+group:
+    - kmmlu
+    - kmmlu_direct
+dataset_path: HAERAE-HUB/KMMLU
+output_type: generate_until
+test_split: test
+fewshot_split: dev
+doc_to_text: "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\n정답："
+doc_to_target: "{{['A', 'B', 'C', 'D'][answer-1]}}"
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+    regexes_to_ignore:
+          - " "
+generation_kwargs:
+  until:
+    - "Q:"
+    - "\n\n"
+    - "</s>"
+    - "."
+  do_sample: false
+  temperature: 0.0
+metadata:
+  version: 2.0

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_accounting.yaml

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+dataset_name: Accounting
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_accounting

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_biology.yaml

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+dataset_name: Biology
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_biology

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_civil_engineering.yaml

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+dataset_name: Civil-Engineering
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_civil_engineering

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_computer_science.yaml

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+dataset_name: Computer-Science
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_computer_science

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_construction.yaml

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+dataset_name: Construction
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_construction

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_ecology.yaml

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+dataset_name: Ecology
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_ecology

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_health.yaml

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+dataset_name: Health
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_health

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_korean_history.yaml

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+dataset_name: Korean-History
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_korean_history

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_machine_design_and_manufacturing.yaml

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+dataset_name: Machine-Design-and-Manufacturing
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_machine_design_and_manufacturing

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_math.yaml

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+dataset_name: Math
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_math

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_patent.yaml

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+dataset_name: Patent
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_patent

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_political_science_and_sociology.yaml

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+dataset_name: Political-Science-and-Sociology
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_political_science_and_sociology

lm-evaluation/lm_eval/tasks/kmmlu/direct/kmmlu_direct_social_welfare.yaml

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+dataset_name: Social-Welfare
+include: _direct_kmmlu_yaml
+task: kmmlu_direct_social_welfare

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_accounting.yaml

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+dataset_name: accounting
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_accounting

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_agricultural_sciences.yaml

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+dataset_name: agricultural_sciences
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_agricultural_sciences

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_aviation_engineering_and_maintenance.yaml

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+dataset_name: aviation_engineering_and_maintenance
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_aviation_engineering_and_maintenance

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_biology.yaml

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+dataset_name: biology
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_biology

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_chemical_engineering.yaml

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+dataset_name: chemical_engineering
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_chemical_engineering

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_civil_engineering.yaml

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+dataset_name: civil_engineering
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_civil_engineering

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_computer_science.yaml

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+dataset_name: computer_science
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_computer_science

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_construction.yaml

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+dataset_name: construction
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_construction

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_ecology.yaml

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+dataset_name: ecology
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_ecology

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_economics.yaml

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+dataset_name: economics
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_economics

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_education.yaml

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+dataset_name: education
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_education

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_electronics_engineering.yaml

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+dataset_name: electronics_engineering
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_electronics_engineering

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_energy_management.yaml

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+dataset_name: energy_management
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_energy_management

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_environmental_science.yaml

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+dataset_name: environmental_science
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_environmental_science

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_fashion.yaml

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+dataset_name: fashion
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_fashion

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_food_processing.yaml

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+dataset_name: food_processing
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_food_processing

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_gas_technology_and_engineering.yaml

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+dataset_name: gas_technology_and_engineering
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_gas_technology_and_engineering

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_geomatics.yaml

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+dataset_name: geomatics
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_geomatics

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_health.yaml

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+dataset_name: health
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_health

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_industrial_engineer.yaml

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+dataset_name: industrial_engineer
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_industrial_engineer

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_interior_architecture_and_design.yaml

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+dataset_name: interior_architecture_and_design
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_interior_architecture_and_design

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_korean_history.yaml

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+dataset_name: korean_history
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_korean_history

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_law.yaml

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+dataset_name: law
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_law

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_machine_design_and_manufacturing.yaml

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+dataset_name: machine_design_and_manufacturing
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_machine_design_and_manufacturing

lm-evaluation/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_maritime_engineering.yaml

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+dataset_name: maritime_engineering
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_maritime_engineering