|
--- |
|
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 |
|
``` |
