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---
language:
- ko
license: cc-by-nc-2.0
size_categories:
- 10K<n<100K
task_categories:
- question-answering
configs:
- 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: data/nurse-train.csv
- split: dev
path: data/nurse-dev.csv
- split: test
path: data/nurse-test.csv
- config_name: pharm
data_files:
- split: train
path: data/pharm-train.csv
- split: dev
path: data/pharm-dev.csv
- split: test
path: data/pharm-test.csv
tags:
- medical
dataset_info:
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
---
# 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
## 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")
```
### Statistics
| Category | # Questions (Train/Dev/Test) |
|------------------------------|------------------------------|
| Doctor | 2,339 (1,890/164/285) |
| Nurse | 1,460 (582/291/587) |
| Pharmacist | 1,546 (632/300/614) |
### 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]
``` |