---
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-*
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: test
    num_bytes: 495590
    num_examples: 2341
  download_size: 291218
  dataset_size: 495590
---

# Dataset Card for JamC-QA

## Dataset Summary

This benchmark test evaluates cultural knowledge related to Japan-specific topics, such as culture and customs, using multiple-choice questions.
This test includes questions across eight categories: Japanese culture, custom, climate, geography, history, government, law and healthcare.
To achieve high accuracy on this test, the model must possess extensive knowledge about Japanese culture.

## Supported Tasks and Leaderboards

| Model | Authors | Micro-average | culture | custom | climate | geography | history | government | law | healthcare | 
| [sarashina2-8x70b](https://huggingface.co/sbintuitions/sarashina2-8x70b) | SB Intuitions Inc., 2024 | 0.7364 | 0.722 | 0.8088 | 0.7855 | 0.6522 | 0.7839 | 0.7719 | 0.6436 | 0.8462 |
| [sarashina2-70b](https://huggingface.co/sbintuitions/sarashina2-70b) | SB Intuitions Inc., 2024 | 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](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4) | Fujii et al., 2024 | 0.695 | 0.6894 | 0.7353 | 0.6185 | 0.5688 | 0.7781 | 0.7719 0.7459 | 0.8462 |
| [RakutenAI-2.0-8x7B](https://huggingface.co/Rakuten/RakutenAI-2.0-8x7B) | Rakuten Group, Inc., 2025 | 0.616 | 0.6056 | 0.6814 | 0.6160 | 0.4855 | 0.6888 | 0.6754 | 0.5941 | 0.6923 |
| [Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) | Mistral AI, 2023 | 0.2772 | 0.2671 | 0.2892 | 0.2618 | 0.2355 | 0.2767 | 0.3509 | 0.3102 | 0.3462 |
| [plamo-100b](https://huggingface.co/pfnet/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](https://huggingface.co/llm-jp/llm-jp-3-8x13b) | | 0.5737 | 0.5839 | 0.6275 | 0.606 | 0.4674 | 0.6110 | 0.6404 | 0.4884 | 0.6538 |
| [Meta-Llama-3.1-405B](https://huggingface.co/meta-llama/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](https://huggingface.co/mgoin/Nemotron-4-340B-Base-hf) | | 0.5600 | 0.5761 | 0.6176 | 0.5062 | 0.4601 | 0.5821 | 0.6491 | 0.5776 | 0.6346 |
| [Qwen2.5-72B](https://huggingface.co/Qwen/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 subtask looks as follows:

## Data Fields

- `qid (str)`: 質問を一意識別するための ID
- `category (str)`: 質問カテゴリ（全 8種）
  - 文化、風習、風土、地理、日本史、行政、法律、医療
- `question (str)`: 質問文
  - 半角カナを除き、全角→半角統一済み
  - 内部に改行 `\n` を含まない
  - 文字列前後の空白は除去済み
- `choice{0..3} (str)`: 選択肢（choice0〜choice3 の 4つ）
  - 半角カナを除き、全角→半角統一済み
  - 内部に改行 `\n` を含まない
  - 文字列前後の空白は除去済み
- `answer_index (int)`: choice{0..3} に対応した正解選択肢のインデックス(0-3)


# Licensing Information

- [CC-BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/)

# Citation Information
```
岡 照晃, 柴田 知秀, 吉田 奈央.
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.
言語処理学会第31回年次大会 (NLP2025) 発表論文集, Q2-18, pp.839-844.
2025年3月11日.
https://www.anlp.jp/proceedings/annual_meeting/2025/pdf_dir/Q2-18.pdf
```