---
configs:
- config_name: doctor
  data_files:
  - split: train
    path: data/doctor-train.csv
  - split: dev
    path: data/doctor-dev.csv
  - split: test
    path: data/doctor-test.csv
- 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
license: cc-by-nc-2.0
task_categories:
- question-answering
language:
- ko
tags:
- medical
size_categories:
- 10K<n<100K
---

# 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

```
sean0042@kaist.ac.kr
```