README.md

metadata

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]