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: 7089
      num_examples: 32
    - name: test
      num_bytes: 515785
      num_examples: 2309
  download_size: 1174968
  dataset_size: 522874

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 categories.

Leaderboard

Evaluation Metric

Accuracy

In this multiple-choice question answering task, the LLM outputs the option string rather than the option label, and accuracy is calculated as the proportion of questions whose output exactly matches the gold correct option string.

Model	Micro-average	culture	custom	regional_identity	geography	history	government	law	healthcare
sarashina2-8x70b	0.7254	0.7141	0.7750	0.7607	0.6544	0.7843	0.7364	0.6321	0.9167
sarashina2-70b	0.7246	0.7188	0.7450	0.7355	0.6728	0.7638	0.7636	0.6656	0.9167
Llama-3.3-Swallow-70B-v0.4	0.6973	0.6891	0.7750	0.5894	0.5662	0.7755	0.7727	0.7826	0.8542
RakutenAI-2.0-8x7B	0.6327	0.6219	0.7250	0.6171	0.5110	0.7143	0.7091	0.5753	0.8125
plamo-100b	0.6033	0.6016	0.6500	0.6373	0.5037	0.6822	0.6091	0.5151	0.6875
Mixtral-8x7B-v0.1-japanese	0.5929	0.6016	0.6700	0.5793	0.4926	0.6122	0.7364	0.5452	0.6667
Meta-Llama-3.1-405B	0.5712	0.5578	0.5450	0.4836	0.5000	0.6793	0.6455	0.6288	0.6875
llm-jp-3.1-8x13b	0.5682	0.5953	0.6350	0.5819	0.4485	0.5889	0.6273	0.5017	0.6250
Nemotron-4-340B-Base	0.5673	0.5734	0.6150	0.5113	0.4669	0.5948	0.7273	0.5819	0.6667
Qwen2.5-72B	0.5271	0.5219	0.5950	0.4257	0.4375	0.6064	0.6091	0.5619	0.6875

Language

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": "影武者",
  "choice1": "羅生門",
  "choice2": "隠し砦の三悪人",
  "choice3": "乱",
  "answer_index": 3,
}

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, and 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: 4 examples per category, intended for few-shot evaluation
test: 2,309 examples in total

Number of Examples:

Category	dev	test
culture	4	640
custom	4	200
regional_identity	4	397
geography	4	272
history	4	343
government	4	110
law	4	299
healthcare	4	48
total	32	2,309

Licensing Information

CC-BY-SA-4.0

How to use

$ python
>>> import datasets
>>> jamcqa = datasets.load_dataset('sbintuitions/JamC-QA', 'v1.0')
>>> print(jamcqa)
DatasetDict({
    test: Dataset({
        features: ['qid', 'category', 'question', 'choice0', 'choice1', 'choice2', 'choice3', 'answer_index'],
        num_rows: 2309
    })
    dev: Dataset({
        features: ['qid', 'category', 'question', 'choice0', 'choice1', 'choice2', 'choice3', 'answer_index'],
        num_rows: 32
    })
})
>>> jamcqa_test = jamcqa['test']
>>> print(jamcqa_test)
Dataset({
    features: ['qid', 'category', 'question', 'choice0', 'choice1', 'choice2', 'choice3', 'answer_index'],
    num_rows: 2309
})
>>> print(jamcqa_test[0])
{'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': '影武者', 'choice1': '羅生門', 'choice2': '隠し砦の三悪人', 'choice3': '乱', 'answer_index': 3}
>>>

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},
}