README.md

metadata

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