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testbed/EleutherAI__lm-evaluation-harness/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?

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_social_welfare.yaml

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+dataset_name: social_welfare
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_social_welfare

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/direct_hard/kmmlu_direct_hard_telecommunications_and_wireless_technology.yaml

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+dataset_name: telecommunications_and_wireless_technology
+include: _direct_hard_kmmlu_yaml
+task: kmmlu_hard_direct_telecommunications_and_wireless_technology

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_civil_engineering.yaml

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+dataset_name: civil_engineering
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_civil_engineering

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_computer_science.yaml

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+dataset_name: computer_science
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_computer_science

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_construction.yaml

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+dataset_name: construction
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_construction

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_ecology.yaml

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+dataset_name: ecology
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_ecology

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_economics.yaml

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+dataset_name: economics
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_economics

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_electrical_engineering.yaml

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+dataset_name: electrical_engineering
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_electrical_engineering

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_energy_management.yaml

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+dataset_name: energy_management
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_energy_management

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_environmental_science.yaml

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+dataset_name: environmental_science
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_environmental_science

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_fashion.yaml

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+dataset_name: fashion
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_fashion

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_geomatics.yaml

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+dataset_name: geomatics
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_geomatics

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_health.yaml

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+dataset_name: health
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_health

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_industrial_engineer.yaml

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+dataset_name: industrial_engineer
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_industrial_engineer

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_information_technology.yaml

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+dataset_name: information_technology
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_information_technology

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_interior_architecture_and_design.yaml

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+dataset_name: interior_architecture_and_design
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_interior_architecture_and_design

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_korean_history.yaml

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+dataset_name: korean_history
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_korean_history

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_law.yaml

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+dataset_name: law
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_law

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_machine_design_and_manufacturing.yaml

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+dataset_name: machine_design_and_manufacturing
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_machine_design_and_manufacturing

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_management.yaml

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+dataset_name: management
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_management

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_maritime_engineering.yaml

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+dataset_name: maritime_engineering
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_maritime_engineering

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_marketing.yaml

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+dataset_name: marketing
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_marketing

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_materials_engineering.yaml

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+dataset_name: materials_engineering
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_materials_engineering

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_math.yaml

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+dataset_name: math
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_math

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_mechanical_engineering.yaml

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+dataset_name: mechanical_engineering
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_mechanical_engineering

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_nondestructive_testing.yaml

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+dataset_name: nondestructive_testing
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_nondestructive_testing

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_patent.yaml

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+dataset_name: patent
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_patent

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_political_science_and_sociology.yaml

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+dataset_name: political_science_and_sociology
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_political_science_and_sociology

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_psychology.yaml

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+dataset_name: psychology
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_psychology

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_public_safety.yaml

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+dataset_name: public_safety
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_public_safety

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_railway_and_automotive_engineering.yaml

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+dataset_name: railway_and_automotive_engineering
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_railway_and_automotive_engineering

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_real_estate.yaml

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+dataset_name: real_estate
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_real_estate

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_refrigerating_machinery.yaml

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+dataset_name: refrigerating_machinery
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_refrigerating_machinery

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_social_welfare.yaml

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+dataset_name: social_welfare
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_social_welfare

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_taxation.yaml

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+dataset_name: taxation
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_taxation

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kmmlu/hard/kmmlu_hard_telecommunications_and_wireless_technology.yaml

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+dataset_name: telecommunications_and_wireless_technology
+include: _hard_kmmlu_yaml
+task: kmmlu_hard_telecommunications_and_wireless_technology

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kobest/README.md

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+# LAMBADA
+
+### Paper
+Title: `KOBEST: Korean Balanced Evaluation of Significant Tasks`
+
+Abstract: https://arxiv.org/abs/2204.04541
+
+A well-formulated benchmark plays a critical role in spurring advancements in the natural language processing (NLP) field, as it allows objective and precise evaluation of diverse models. As modern language models (LMs) have become more elaborate and sophisticated, more difficult benchmarks that require linguistic knowledge and reasoning have been proposed. However, most of these benchmarks only support English, and great effort is necessary to construct benchmarks for other low resource languages. To this end, we propose a new benchmark named Korean balanced evaluation of significant tasks (KoBEST), which consists of five Korean-language downstream tasks. Professional Korean linguists designed the tasks that require advanced Korean linguistic knowledge. Moreover, our data is purely annotated by humans and thoroughly reviewed to guarantee high data quality. We also provide baseline models and human performance results. Our dataset is available on the Huggingface.
+
+
+Homepage: https://huggingface.co/datasets/skt/kobest_v1
+
+### Groups and Tasks
+
+#### Groups
+
+- `kobest`
+
+#### Tasks
+
+- `kobest_boolq`
+- `kobest_copa`
+- `kobest_hallawag`
+- `kobest_sentineg`
+- `kobest_wic`
+
+
+### Citation
+
+@misc{
+    author={Dohyeong Kim, Myeongjun Jang, Deuk Sin Kwon, Eric Davis},
+    title={KOBEST: Korean Balanced Evaluation of Significant Tasks},
+    DOI={https://doi.org/10.48550/arXiv.2204.04541},
+    publisher={arXiv},
+    year={2022},
+    month={Apr}
+}

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kobest/kobest_boolq.yaml

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+tag:
+  - kobest
+task: kobest_boolq
+dataset_path: skt/kobest_v1
+dataset_name: boolq
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+test_split: test
+doc_to_text: "{{paragraph}} 질문: {{question}} 답변: "
+doc_to_target: "{{label}}"
+doc_to_choice: ["아니오", "예"]
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: True
+  - metric: f1
+    aggregation: !function utils.macro_f1_score
+    average: macro
+    hf_evaluate: true
+    higher_is_better: True
+metadata:
+  version: 1.0

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kobest/kobest_copa.yaml

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+tag:
+  - kobest
+task: kobest_copa
+dataset_path: skt/kobest_v1
+dataset_name: copa
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+test_split: test
+doc_to_text: !function utils.copa_doc_to_text
+doc_to_target: !function utils.copa_doc_to_target
+doc_to_choice: !function utils.copa_doc_to_choice
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: True
+  - metric: f1
+    aggregation: !function utils.macro_f1_score
+    average: macro
+    hf_evaluate: true
+    higher_is_better: True
+metadata:
+  version: 1.0

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kobest/kobest_hellaswag.yaml

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+tag:
+  - kobest
+task: kobest_hellaswag
+dataset_path: skt/kobest_v1
+dataset_name: hellaswag
+training_split: train
+validation_split: validation
+output_type: multiple_choice
+test_split: test
+doc_to_text: "{{query}}"
+doc_to_target: "{{label}}"
+process_docs: !function utils.hellaswag_process_doc
+doc_to_choice: "choices"
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: True
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: True
+  - metric: f1
+    aggregation: !function utils.macro_f1_score
+    average: macro
+    hf_evaluate: true
+    higher_is_better: True
+metadata:
+  version: 1.0

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kobest/kobest_sentineg.yaml

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+tag:
+  - kobest
+task: kobest_sentineg
+dataset_path: skt/kobest_v1
+dataset_name: sentineg
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+test_split: test
+doc_to_text: !function utils.sentineg_doc_to_text
+doc_to_target: "{{label}}"
+doc_to_choice: ["부정", "긍정"]
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: True
+  - metric: f1
+    aggregation: !function utils.macro_f1_score
+    average: macro
+    hf_evaluate: true
+    higher_is_better: True
+metadata:
+  version: 1.0
+dataset_kwargs:
+  trust_remote_code: true

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kobest/kobest_wic.yaml

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+tag:
+  - kobest
+task: kobest_wic
+dataset_path: skt/kobest_v1
+dataset_name: wic
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+test_split: test
+doc_to_text: !function utils.wic_doc_to_text
+doc_to_target: "{{label}}"
+doc_to_choice: ['아니오', '예']
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: True
+  - metric: f1
+    aggregation: !function utils.macro_f1_score
+    average: macro
+    hf_evaluate: true
+    higher_is_better: True
+metadata:
+  version: 1.0
+dataset_kwargs:
+  trust_remote_code: true

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kobest/utils.py

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+from datasets import Dataset
+
+
+def copa_doc_to_text(doc: dict) -> str:
+    connector = {"원인": " 왜냐하면", "결과": " 그래서"}[doc["question"].strip()]
+    return f"""{doc["premise"]} {connector}"""
+
+
+def copa_doc_to_target(doc: dict) -> str:
+    correct_choice = doc["alternative_1"] if doc["label"] == 0 else doc["alternative_2"]
+    return f"""{correct_choice}"""
+
+
+def copa_doc_to_choice(doc: dict) -> list:
+    return [f"""{doc["alternative_1"]}""", f"""{doc["alternative_2"]}"""]
+
+
+def sentineg_doc_to_text(doc: dict):
+    return f"""문장: {doc["sentence"]} 긍부정:"""
+
+
+def wic_doc_to_text(doc: dict) -> str:
+    return f"""문장1: {doc["context_1"]} 문장2: {doc["context_2"]} 두 문장에서 {doc["word"]}가 같은 뜻으로 쓰였나?"""
+
+
+def hellaswag_process_doc(doc: Dataset) -> Dataset:
+    def preprocessor(dataset):
+        return {
+            "query": f"""문장: {dataset["context"]}""",
+            "choices": [
+                dataset["ending_1"],
+                dataset["ending_2"],
+                dataset["ending_3"],
+                dataset["ending_4"],
+            ],
+            "gold": int(dataset["label"]),
+        }
+
+    return doc.map(preprocessor)
+
+
+def macro_f1_score(items):
+    from sklearn.metrics import f1_score
+
+    unzipped_list = list(zip(*items))
+    golds = unzipped_list[0]
+    preds = unzipped_list[1]
+    fscore = f1_score(golds, preds, average="macro")
+    return fscore

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kormedmcqa/README.md

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+# KorMedMCQA
+
+### Paper
+
+Title: `KorMedMCQA: Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations`
+
+Abstract: `We introduce KorMedMCQA, the first Korean multiple-choice question answering (MCQA) benchmark derived from Korean healthcare professional licensing examinations, covering from the year 2012 to year 2023. This dataset consists of a selection of questions from the license examinations for doctors, nurses, and pharmacists, featuring a diverse array of subjects. We conduct baseline experiments on various large language models, including proprietary/open-source, multilingual/Korean-additional pretrained, and clinical context pretrained models, highlighting the potential for further enhancements. We make our data publicly available on HuggingFace and provide a evaluation script via LM-Harness, inviting further exploration and advancement in Korean healthcare environments.`
+
+
+Paper : https://arxiv.org/abs/2403.01469
+
+Homepage: https://huggingface.co/datasets/sean0042/KorMedMCQA
+
+
+### Citation
+
+```
+@article{kweon2024kormedmcqa,
+      title={KorMedMCQA: Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations},
+      author={Sunjun Kweon and Byungjin Choi and Minkyu Kim and Rae Woong Park and Edward Choi},
+      journal={arXiv preprint arXiv:2403.01469},
+      year={2024}
+}
+```
+
+### Groups and Tasks
+
+* `kormedmcqa`: Runs `kormedmcqa_doctor`, `kormedmcqa_nurse`, and `kormedmcqa_pharm`.
+
+#### Tasks
+
+* `kormedmcqa_doctor`: `Official Korean Doctor Examination`
+* `kormedmcqa_nurse`: `Official Korean Nurse Examination`
+* `kormedmcqa_pharm`: `Official Korean Pharmacist Examination`
+
+### 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?

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kormedmcqa/_kormedmcqa.yaml

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+group: kormedmcqa
+task:
+  - kormedmcqa_doctor
+  - kormedmcqa_nurse
+  - kormedmcqa_pharm
+aggregate_metric_list:
+  - metric: exact_match
+    aggregation: mean
+    weight_by_size: true
+metadata:
+  version: 0.0

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kormedmcqa/kormedmcqa_doctor.yaml

@@ -0,0 +1,26 @@
+task : kormedmcqa_doctor
+dataset_path : sean0042/KorMedMCQA
+dataset_name : doctor
+test_split : test
+fewshot_split : dev
+fewshot_config:
+  sampler: first_n
+output_type: generate_until
+doc_to_text: "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nE. {{E}}\n정답："
+doc_to_target: "{{['A', 'B', 'C', 'D', 'E'][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

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kormedmcqa/kormedmcqa_nurse.yaml

@@ -0,0 +1,26 @@
+task : kormedmcqa_nurse
+dataset_path : sean0042/KorMedMCQA
+dataset_name : nurse
+test_split : test
+fewshot_split : dev
+fewshot_config:
+  sampler: first_n
+output_type: generate_until
+doc_to_text: "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nE. {{E}}\n정답："
+doc_to_target: "{{['A', 'B', 'C', 'D', 'E'][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

testbed/EleutherAI__lm-evaluation-harness/lm_eval/tasks/kormedmcqa/kormedmcqa_pharm.yaml

@@ -0,0 +1,26 @@
+task : kormedmcqa_pharm
+dataset_path : sean0042/KorMedMCQA
+dataset_name : pharm
+test_split : test
+fewshot_split : dev
+fewshot_config:
+  sampler: first_n
+output_type: generate_until
+doc_to_text: "{{question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nE. {{E}}\n정답："
+doc_to_target: "{{['A', 'B', 'C', 'D', 'E'][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