README.md

---
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: 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: 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

## 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]
```