Datasets:

sbintuitions
/

JamC-QA

license: cc-by-sa-4.0
task_categories:
  - question-answering
  - multiple-choice
language:
  - ja
configs:
  - config_name: v1.0
    data_files:
      - split: test
        path: v1.0/test-*
      - split: dev
        path: v1.0/dev-*
dataset_info:
  config_name: v1.0
  features:
    - name: qid
      dtype: string
    - name: category
      dtype: string
    - name: question
      dtype: string
    - name: choice0
      dtype: string
    - name: choice1
      dtype: string
    - name: choice2
      dtype: string
    - name: choice3
      dtype: string
    - name: answer_index
      dtype: int64
  splits:
    - name: dev
      num_bytes: 7097
      num_examples: 32
    - name: test
      num_bytes: 516185
      num_examples: 2309
  download_size: 588759
  dataset_size: 523282

Dataset Card for JamC-QA

English/Japanese

Dataset Summary

This benchmark evaluates knowledge specific to Japan through multiple-choice questions. It covers eight categories: culture, custom, regional identity, geography, history, government, law, and healthcare. Achieving high performance requires broad and detailed understanding of Japan across these domains.

Leaderboard

Exact match score:

Model	Micro-average	culture	custom	regional identity	geography	history	government	law	healthcare
sarashina2-8x70b	0.7364	0.7220	0.8088	0.7855	0.6522	0.7839	0.7719	0.6436	0.8462
sarashina2-70b	0.7245	0.6988	0.7892	0.7556	0.6558	0.7781	0.7544	0.6733	0.7885
Llama-3.3-Swallow-70B-v0.4	0.6950	0.6894	0.7353	0.6185	0.5688	0.7781	0.7719	0.7459	0.8462
RakutenAI-2.0-8x7B	0.6160	0.6056	0.6814	0.6160	0.4855	0.6888	0.6754	0.5941	0.6923
Mixtral-8x7B-v0.1-japanese	0.5950	0.5885	0.7500	0.5985	0.4601	0.6052	0.6404	0.5710	0.7308
plamo-100b	0.5908	0.6102	0.6422	0.6384	0.4565	0.6398	0.5526	0.5182	0.6731
llm-jp-3.1-8x13b	0.5737	0.5839	0.6275	0.6060	0.4674	0.6110	0.6404	0.4884	0.6538
Meta-Llama-3.1-405B	0.5724	0.5699	0.5245	0.4688	0.5435	0.6571	0.6579	0.6403	0.5962
Nemotron-4-340B-Base	0.5600	0.5761	0.6176	0.5062	0.4601	0.5821	0.6491	0.5776	0.6346
Qwen2.5-72B	0.5421	0.5419	0.6324	0.4763	0.4746	0.5677	0.6053	0.5644	0.6154

Languages

Japanese

Dataset Structure

Data Instances

An example from culture category looks as follows:

{
  "qid": "jamcqa_test_culture_00001",
  "category": "culture",
  "question": "「狂った世で気が狂うなら気は確かだ」の名言を残した映画はどれ?",
  "choice0": "乱",
  "choice1": "羅生門",
  "choice2": "隠し砦の三悪人",
  "choice3": "影武者",
  "answer_index": 0,
}

Data Fields

qid (str): A unique identifier for each question.
category (str): The category of the question.
- culture, custom, regional identity, geography, history, government, law, healthcare
question (str): The question text.
- Converted from full-width to half-width characters, excluding katakana characters.
- Does not contain any line breaks (\n).
- Leading and trailing whitespace is removed.
choice{0..3} (str): Four answer options (choice0 to choice3).
- Converted from full-width to half-width characters, excluding katakana characters.
- Does not contain any line breaks (\n).
- Leading and trailing whitespace is removed.
answer_index (int): The index of the correct answer among choice0 to choice3 (0–3).

Data Splits

dev: 7 examples, meant for few-shot setting
test: there are 2,341 examples

Category	Number of Questions
culture	644
custom	204
regional identity	401
geography	276
history	347
government	114
law	303
healthcare	52
total	2,341

Licensing Information

CC-BY-SA-4.0

How to use

$ python
>>> import datasets
>>> jamcqa_test = datasets.load_dataset('sbintuitions/JamC-QA', 'v1.0', split='test')
>>> print(jamcqa_test)
Dataset({
    features: ['qid', 'category', 'question', 'choice0', 'choice1', 'choice2', 'choice3', 'answer_index'],
    num_rows: 2341
})
>>> print(jamcqa_test[0])
{'qid': 'jamcqa_test_culture_00001', 'category': '文化', 'question': '「狂った世で気が狂うなら気は確かだ」の名言を残した映画はどれ?', 'choice0': '乱', 'choice1': '羅生門', 'choice2': '隠し砦の三悪人', 'choice3': '影武者', 'answer_index': 0}
>>>

Citation Information

@inproceedings{Oka2025,
  author={岡 照晃, 柴田 知秀, 吉田 奈央},
  title={JamC-QA: 日本固有の知識を問う多肢選択式質問応答ベンチマークの構築},
  year={2025},
  month={March},
  booktitle={言語処理学会第31回年次大会(NLP2025)},
  pages={839--844},
}