| | --- |
| | language: |
| | - ko |
| | license: cc-by-nc-2.0 |
| | size_categories: |
| | - 10K<n<100K |
| | task_categories: |
| | - question-answering |
| | configs: |
| | - config_name: dentist |
| | data_files: |
| | - split: train |
| | path: dentist/train-* |
| | - split: dev |
| | path: dentist/dev-* |
| | - split: test |
| | path: dentist/test-* |
| | - split: fewshot |
| | path: dentist/fewshot-* |
| | - config_name: doctor |
| | data_files: |
| | - split: train |
| | path: doctor/train-* |
| | - split: dev |
| | path: doctor/dev-* |
| | - split: test |
| | path: doctor/test-* |
| | - split: fewshot |
| | path: doctor/fewshot-* |
| | - config_name: nurse |
| | data_files: |
| | - split: train |
| | path: nurse/train-* |
| | - split: dev |
| | path: nurse/dev-* |
| | - split: test |
| | path: nurse/test-* |
| | - split: fewshot |
| | path: nurse/fewshot-* |
| | - config_name: pharm |
| | data_files: |
| | - split: train |
| | path: pharm/train-* |
| | - split: dev |
| | path: pharm/dev-* |
| | - split: test |
| | path: pharm/test-* |
| | - split: fewshot |
| | path: pharm/fewshot-* |
| | tags: |
| | - medical |
| | dataset_info: |
| | - config_name: dentist |
| | features: |
| | - name: subject |
| | dtype: string |
| | - name: year |
| | dtype: int64 |
| | - name: period |
| | dtype: int64 |
| | - name: q_number |
| | dtype: int64 |
| | - name: question |
| | dtype: string |
| | - name: A |
| | dtype: string |
| | - name: B |
| | dtype: string |
| | - name: C |
| | dtype: string |
| | - name: D |
| | dtype: string |
| | - name: E |
| | dtype: string |
| | - name: answer |
| | dtype: int64 |
| | - name: cot |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 116376 |
| | num_examples: 297 |
| | - name: dev |
| | num_bytes: 119727 |
| | num_examples: 304 |
| | - name: test |
| | num_bytes: 330325 |
| | num_examples: 811 |
| | - name: fewshot |
| | num_bytes: 4810 |
| | num_examples: 5 |
| | download_size: 374097 |
| | dataset_size: 571238 |
| | - config_name: doctor |
| | features: |
| | - name: subject |
| | dtype: string |
| | - name: year |
| | dtype: int64 |
| | - name: period |
| | dtype: int64 |
| | - name: q_number |
| | dtype: int64 |
| | - name: question |
| | dtype: string |
| | - name: A |
| | dtype: string |
| | - name: B |
| | dtype: string |
| | - name: C |
| | dtype: string |
| | - name: D |
| | dtype: string |
| | - name: E |
| | dtype: string |
| | - name: answer |
| | dtype: int64 |
| | - name: cot |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 1137189 |
| | num_examples: 1890 |
| | - name: dev |
| | num_bytes: 111294 |
| | num_examples: 164 |
| | - name: test |
| | num_bytes: 315104 |
| | num_examples: 435 |
| | - name: fewshot |
| | num_bytes: 8566 |
| | num_examples: 5 |
| | download_size: 871530 |
| | dataset_size: 1572153 |
| | - config_name: nurse |
| | features: |
| | - name: subject |
| | dtype: string |
| | - name: year |
| | dtype: int64 |
| | - name: period |
| | dtype: int64 |
| | - name: q_number |
| | dtype: int64 |
| | - name: question |
| | dtype: string |
| | - name: A |
| | dtype: string |
| | - name: B |
| | dtype: string |
| | - name: C |
| | dtype: string |
| | - name: D |
| | dtype: string |
| | - name: E |
| | dtype: string |
| | - name: answer |
| | dtype: int64 |
| | - name: cot |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 219983 |
| | num_examples: 582 |
| | - name: dev |
| | num_bytes: 110210 |
| | num_examples: 291 |
| | - name: test |
| | num_bytes: 327186 |
| | num_examples: 878 |
| | - name: fewshot |
| | num_bytes: 6324 |
| | num_examples: 5 |
| | download_size: 419872 |
| | dataset_size: 663703 |
| | - config_name: pharm |
| | features: |
| | - name: subject |
| | dtype: string |
| | - name: year |
| | dtype: int64 |
| | - name: period |
| | dtype: int64 |
| | - name: q_number |
| | dtype: int64 |
| | - name: question |
| | dtype: string |
| | - name: A |
| | dtype: string |
| | - name: B |
| | dtype: string |
| | - name: C |
| | dtype: string |
| | - name: D |
| | dtype: string |
| | - name: E |
| | dtype: string |
| | - name: answer |
| | dtype: int64 |
| | - name: cot |
| | dtype: string |
| | splits: |
| | - name: train |
| | num_bytes: 272256 |
| | num_examples: 632 |
| | - name: dev |
| | num_bytes: 139900 |
| | num_examples: 300 |
| | - name: test |
| | num_bytes: 412847 |
| | num_examples: 885 |
| | - name: fewshot |
| | num_bytes: 6324 |
| | num_examples: 5 |
| | download_size: 504010 |
| | dataset_size: 831327 |
| | --- |
| | |
| | # KorMedMCQA : Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations |
| |
|
| | We present KorMedMCQA, the first Korean Medical Multiple-Choice Question |
| | Answering benchmark, derived from professional healthcare licensing |
| | examinations conducted in Korea between 2012 and 2024. The dataset contains |
| | 7,469 questions from examinations for doctor, nurse, pharmacist, and dentist, |
| | covering a wide range of medical disciplines. We evaluate the performance of 59 |
| | large language models, spanning proprietary and open-source models, |
| | multilingual and Korean-specialized models, and those fine-tuned for clinical |
| | applications. Our results show that applying Chain of Thought (CoT) reasoning |
| | can enhance the model performance by up to 4.5% compared to direct answering |
| | approaches. We also investigate whether MedQA, one of the most widely used |
| | medical benchmarks derived from the U.S. Medical Licensing Examination, can |
| | serve as a reliable proxy for evaluating model performance in other regions-in |
| | this case, Korea. Our correlation analysis between model scores on KorMedMCQA |
| | and MedQA reveals that these two benchmarks align no better than benchmarks |
| | from entirely different domains (e.g., MedQA and MMLU-Pro). This finding |
| | underscores the substantial linguistic and clinical differences between Korean |
| | and U.S. medical contexts, reinforcing the need for region-specific medical QA |
| | benchmarks. |
| |
|
| | Paper : https://arxiv.org/abs/2403.01469 |
| |
|
| | ## Notice |
| |
|
| | We have made the following updates to the KorMedMCQA dataset: |
| |
|
| | 1. **Dentist Exam**: Incorporated exam questions from 2021 to 2024. |
| | 2. **Updated Test Sets**: Added the 2024 exam questions for the doctor, nurse, and pharmacist test sets. |
| | 3. **Few-Shot Split**: Introduced a `fewshot` split, containing 5 shots from each validation set. |
| | 4. **Chain-of-Thought(CoT)**: In each exam's few-shot split (`cot` column), there is an answer with reasoning annotated by professionals |
| |
|
| |
|
| | ## Dataset Details |
| |
|
| | ### Languages |
| |
|
| | Korean |
| |
|
| | ### Subtask |
| |
|
| | ``` |
| | from datasets import load_dataset |
| | doctor = load_dataset(path = "sean0042/KorMedMCQA",name = "doctor") |
| | nurse = load_dataset(path = "sean0042/KorMedMCQA",name = "nurse") |
| | pharmacist = load_dataset(path = "sean0042/KorMedMCQA",name = "pharm") |
| | dentist = load_dataset(path = "sean0042/KorMedMCQA",name = "dentist") |
| | ``` |
| |
|
| | ### Statistics |
| |
|
| | | Category | # Questions (Train/Dev/Test) | |
| | |------------------------------|------------------------------| |
| | | Doctor | 2,489 (1,890/164/435) | |
| | | Nurse | 1,751 (582/291/878) | |
| | | Pharmacist | 1,817 (632/300/885) | |
| | | Dentist | 1,412 (297/304/811) | |
| |
|
| | ### Data Fields |
| |
|
| |
|
| | - `subject`: doctor, nurse, or pharm |
| | - `year`: year of the examination |
| | - `period`: period of the examination |
| | - `q_number`: question number of the examination |
| | - `question`: question |
| | - `A`: First answer choice |
| | - `B`: Second answer choice |
| | - `C`: Third answer choice |
| | - `D`: Fourth answer choice |
| | - `E`: Fifth answer choice |
| | - `cot` : Answer with reasoning annotated by professionals (only available in fewshot split) |
| | - `answer` : Answer (1 to 5). 1 denotes answer A, and 5 denotes answer E |
| |
|
| |
|
| | ## Contact |
| |
|
| | ``` |
| | sean0042@kaist.ac.kr |
| | ``` |
