--- 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](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 In this multiple-choice QA task, the LLM outputs the option string rather than the option label. The following table shows the proportion of outputs that exactly match the gold option string. | Model | All | culture | custom | regional_identity | geography | history | government | law | healthcare | |:---|----|---:|---:|---:|---:|---:|---:|---:|---:| | [sarashina2-8x70b](https://huggingface.co/sbintuitions/sarashina2-8x70b) | **0.725** | 0.714 | **0.775** | **0.761** | 0.654 | **0.784** | 0.736 | 0.632 | **0.917** | | [sarashina2-70b](https://huggingface.co/sbintuitions/sarashina2-70b) | **0.725** | **0.719** | 0.745 | 0.736 | **0.673** | 0.764 | 0.764 | 0.666 | **0.917** | | [Llama-3.3-Swallow-70B-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4) | 0.697 | 0.689 | **0.775** | 0.589 | 0.566 | 0.776 | **0.773** | **0.783** | 0.854 | | [RakutenAI-2.0-8x7B](https://huggingface.co/Rakuten/RakutenAI-2.0-8x7B) | 0.633 | 0.622 | 0.725 | 0.617 | 0.511 | 0.714 | 0.709 | 0.575 | 0.813 | | [plamo-100b](https://huggingface.co/pfnet/plamo-100b) | 0.603 | 0.602 | 0.650 | 0.637 | 0.504 | 0.682 | 0.609 | 0.515 | 0.688 | | [Mixtral-8x7B-v0.1-japanese](https://huggingface.co/abeja/Mixtral-8x7B-v0.1-japanese) | 0.593 | 0.602 | 0.670 | 0.579 | 0.493 | 0.612 | 0.736 | 0.545 | 0.667 | | [Meta-Llama-3.1-405B](https://huggingface.co/meta-llama/Llama-3.1-405B) | 0.571 | 0.558 | 0.545 | 0.484 | 0.500 | 0.679 | 0.646 | 0.629 | 0.688 | | [llm-jp-3.1-8x13b](https://huggingface.co/llm-jp/llm-jp-3-8x13b) | 0.568 | 0.595 | 0.635 | 0.582 | 0.449 | 0.589 | 0.627 | 0.502 | 0.625 | | [Nemotron-4-340B-Base](https://huggingface.co/mgoin/Nemotron-4-340B-Base-hf) | 0.567 | 0.573 | 0.615 | 0.511 | 0.467 | 0.595 | 0.727 | 0.582 | 0.667 | | [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) | 0.527 | 0.522 | 0.595 | 0.426 | 0.438 | 0.606 | 0.609 | 0.562 | 0.688 | ## 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](https://creativecommons.org/licenses/by-sa/4.0/) # Usage ## Dataset Loading ```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} >>> ``` ## Evaluation with FlexEval You can easily use [FlexEval](https://github.com/sbintuitions/flexeval) (version 0.13.3 or later) to evaluate the JamC-QA score by simply replacing `commonsense_qa` with `jamcqa` in the [Quickstart](https://github.com/sbintuitions/flexeval?tab=readme-ov-file#quick-start) guide. ### Run Command ```python flexeval_lm \ --language_model HuggingFaceLM \ --language_model.model "sbintuitions/sarashina2.2-0.5b" \ --language_model.default_gen_kwargs "{ do_sample: false }" \ --eval_setup "jamcqa" \ --save_dir "results/jamcqa" ``` `--language_model.default_gen_kwargs "{ do_sample: false }"` disables sampling and performs [greedy search](https://huggingface.co/docs/transformers/generation_strategies#greedy-search). ### Output The expected output is as follows: ``` 2025-09-03 15:48:24.633 | INFO | flexeval.core.evaluate_generation:evaluate_generation:92 - {'exact_match': 0.2368990905153746, 'finish_reason_ratio-stop': 1.0, 'avg_output_length': 6.94283239497618, 'max_output_length': 93, 'min_output_length': 2} 2025-09-03 15:48:24.666 | INFO | flexeval.scripts.flexeval_lm:main:191 - Elapsed time: 247.23 sec 2025-09-03 15:48:24.670 | INFO | flexeval.core.result_recorder.local_recorder:record_metrics:84 - Saved the metrics to results/jamcqa/metrics.json 2025-09-03 15:48:24.724 | INFO | flexeval.core.result_recorder.local_recorder:record_model_outputs:95 - Saved the outputs to results/jamcqa/outputs.jsonl 2025-09-03 15:48:24.896 | INFO | flexeval.core.language_model.hf_lm:cleanup_resources:557 - Cleaning up CUDA resources... ``` # 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}, } ```