|
--- |
|
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 |
|
splits: |
|
- name: train |
|
num_bytes: 115188 |
|
num_examples: 297 |
|
- name: dev |
|
num_bytes: 118511 |
|
num_examples: 304 |
|
- name: test |
|
num_bytes: 327081 |
|
num_examples: 811 |
|
- name: fewshot |
|
num_bytes: 1949.1940789473683 |
|
num_examples: 5 |
|
download_size: 367138 |
|
dataset_size: 562729.1940789474 |
|
- 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 |
|
splits: |
|
- name: train |
|
num_bytes: 1129629 |
|
num_examples: 1890 |
|
- name: dev |
|
num_bytes: 110638 |
|
num_examples: 164 |
|
- name: test |
|
num_bytes: 313364 |
|
num_examples: 435 |
|
- name: fewshot |
|
num_bytes: 3373.109756097561 |
|
num_examples: 5 |
|
download_size: 861550 |
|
dataset_size: 1557004.1097560977 |
|
- 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 |
|
splits: |
|
- name: train |
|
num_bytes: 217655 |
|
num_examples: 582 |
|
- name: dev |
|
num_bytes: 109046 |
|
num_examples: 291 |
|
- name: test |
|
num_bytes: 323674 |
|
num_examples: 878 |
|
- name: fewshot |
|
num_bytes: 1873.6426116838488 |
|
num_examples: 5 |
|
download_size: 411733 |
|
dataset_size: 652248.6426116838 |
|
- 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 |
|
splits: |
|
- name: train |
|
num_bytes: 269728 |
|
num_examples: 632 |
|
- name: dev |
|
num_bytes: 138700 |
|
num_examples: 300 |
|
- name: test |
|
num_bytes: 409307 |
|
num_examples: 885 |
|
- name: fewshot |
|
num_bytes: 2311.6666666666665 |
|
num_examples: 5 |
|
download_size: 494145 |
|
dataset_size: 820046.6666666666 |
|
--- |
|
|
|
# KorMedMCQA : Multi-Choice Question Answering Benchmark for Korean Healthcare Professional Licensing Examinations |
|
|
|
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 |
|
|
|
## Notice |
|
|
|
In collaboration with [Hugging Face](https://huggingface.co./datasets/ChuGyouk/KorMedMCQA_edited), 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 "few-shot" split, containing 5 shots from each validation set. |
|
|
|
|
|
## 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 |
|
- `answer` : Answer (1 to 5). 1 denotes answer A, and 5 denotes answer E |
|
|
|
|
|
## Contact |
|
|
|
``` |
|
[email protected] |
|
``` |
