README.md

---
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: 886472
  dataset_size: 522874
---

# Dataset Card for JamC-QA

English/[Japanese](README_ja.md)

## 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](https://huggingface.co/sbintuitions/sarashina2-8x70b) | **0.7254** | 0.7141 | **0.7750** | **0.7607** | 0.6544 | **0.7843** | 0.7364 | 0.6321 | **0.9167** |
| [sarashina2-70b](https://huggingface.co/sbintuitions/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](https://huggingface.co/tokyotech-llm/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](https://huggingface.co/Rakuten/RakutenAI-2.0-8x7B) | 0.6327 | 0.6219 | 0.7250 | 0.6171 | 0.5110 | 0.7143 | 0.7091 | 0.5753 | 0.8125 |
| [plamo-100b](https://huggingface.co/pfnet/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](https://huggingface.co/abeja/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](https://huggingface.co/meta-llama/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](https://huggingface.co/llm-jp/llm-jp-3-8x13b) | 0.5682 | 0.5953 | 0.6350 | 0.5819 | 0.4485 | 0.5889 | 0.6273 | 0.5017 | 0.6250 |
| [Nemotron-4-340B-Base](https://huggingface.co/mgoin/Nemotron-4-340B-Base-hf) | 0.5673 | 0.5734 | 0.6150 | 0.5113 | 0.4669 | 0.5948 | 0.7273 | 0.5819 | 0.6667 |
| [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) | 0.5271 | 0.5219 | 0.5950 | 0.4257 | 0.4375 | 0.6064 | 0.6091 | 0.5619 | 0.6875 |

## 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": "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](https://creativecommons.org/licenses/by-sa/4.0/)

# How to use

```python
$ 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},
}
```