metadata
language:
- ko
license: cc-by-nc-2.0
size_categories:
- 10K<n<100K
task_categories:
- question-answering
configs:
- config_name: dentist
data_files:
- split: train
path: dentist/train-*
- split: dev
path: dentist/dev-*
- split: test
path: dentist/test-*
- split: fewshot
path: dentist/fewshot-*
- 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: dentist
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: 115188
num_examples: 297
- name: dev
num_bytes: 118511
num_examples: 304
- name: test
num_bytes: 327081
num_examples: 811
- name: fewshot
num_bytes: 1949.1940789473683
num_examples: 5
download_size: 367138
dataset_size: 562729.1940789474
- 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:
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- name: dev
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num_examples: 164
- name: test
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num_examples: 435
- name: fewshot
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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
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- name: dev
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- name: test
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- name: fewshot
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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
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num_examples: 632
- name: dev
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num_examples: 300
- name: test
num_bytes: 409307
num_examples: 885
- name: fewshot
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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
Notice
In collaboration with Hugging Face, we have made the following updates to the KorMedMCQA dataset:
- Dentist Exam: Incorporated exam questions from 2021 to 2024.
- Updated Test Sets: Added the 2024 exam questions for the doctor, nurse, and pharmacist test sets.
- Few-Shot Split: Introduced a "few-shot" split, containing 5 shots from each validation set.
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")
dentist = load_dataset(path = "sean0042/KorMedMCQA",name = "dentist")
Statistics
| Category | # Questions (Train/Dev/Test) |
|---|---|
| Doctor | 2,489 (1,890/164/435) |
| Nurse | 1,751 (582/291/878) |
| Pharmacist | 1,817 (632/300/885) |
| Dentist | 1,412 (297/304/811) |
Data Fields
subject: doctor, nurse, or pharmyear: year of the examinationperiod: period of the examinationq_number: question number of the examinationquestion: questionA: First answer choiceB: Second answer choiceC: Third answer choiceD: Fourth answer choiceE: Fifth answer choiceanswer: Answer (1 to 5). 1 denotes answer A, and 5 denotes answer E
Contact
[email protected]