README.md

---
license: cc-by-sa-4.0
task_categories:
- question-answering
language:
- ja
viewer: true
columns:
- name: problem_id
  type: string
- name: problem_text
  type: string
- name: choices
  type: list[string]
- name: text_only
  type: bool
- name: answer
  type: list[string]
- name: comment
  type: string
- name: num_images
  type: int
---

# YakugakuQA

<!-- Provide a quick summary of the dataset. -->

YakugakuQA is a question answering dataset, consisting of 13 years (2012-2024) of past questions and answers from the Japanese National License Examination for Pharmacists. It contains over 4K pairs of questions, answers, and commentaries.

**2025-5-29: Leaderboard added.**  

**2025-2-17: Image data added.**  

**2024-12-10: Dataset release.**


## Leaderboard
3-shot Accuracy (%)

|| [YakugakuQA](https://huggingface.co/datasets/EQUES/YakugakuQA/) | [IgakuQA](https://github.com/jungokasai/IgakuQA)|
| ---- | ---- | ---- |
| o1-preview | 87.9 | |
| GPT-4o | 83.6 | 86.6 | 
| [pfnet/Preferred-MedLLM-Qwen-72B](https://huggingface.co/pfnet/Preferred-MedLLM-Qwen-72B) | 77.2 | |
| [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) | 73.6 | |
| [google/medgemma-27b-text-it](https://huggingface.co/google/medgemma-27b-text-it) | 62.2 (*)| |
| [EQUES/JPharmatron-7B](https://huggingface.co/EQUES/JPharmatron-7B) | 62.0 | 64.7 |
| [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) (**) | 59.9 |  |

(*) Several issues in instruction-following, e.g., think and reason too much to reach token limit.  
(**) enable_thinking=False for fair evaluation.


## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

- **Curated by:** EQUES Inc.
- **Funded by [optional]:** [GENIAC Project](https://www.meti.go.jp/policy/mono_info_service/geniac/index.html)
- **Shared by [optional]:**
- **Language(s) (NLP):** Japanese
- **License:** cc-by-sa-4.0

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

YakugakuQA is intended to be used as a benchmark for evaluating the knowledge of large language models (LLMs) in the field of pharmacy.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

Any usage except above.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

YakugakuQA consists of two files: `data.jsonl`, which contains the questions, answers, and commentaries, and `metadata.jsonl`, which holds supplementary information about the question categories and additional details related to the answers.

### data.jsonl

- "problem_id" : unique ID, represented by a six-digit integer. The higher three digits indicate the exam number, while the lower three digits represent the question number within that specific exam.
- "problem_text" : problem statement.
- "choices" : choices corresponding to each question. Note that the Japanese National License Examination for Pharmacists is a multiple-choice format examination.
- "text_only" : whether the question includes images or tables. The corresponding images or tables are not included in this dataset, even if `text_only` is marked as `false`.
- "answer" : list of indices of the correct choices. Note the following points:
  - the choices are 1-indexed.
  - multiple choices may be included, depending on the question format.
  - "解なし" indicates there is no correct choice. The reason for this is documented in `metadata.jsonl` in most cases.
- "comment" : commentary text.
- "num_images" : number of images included in the question.

### metadata.jsonl

- "problem_id" : see above.
- "category" : question caterogy. One of the `["Physics", "Chemistry", "Biology", "Hygiene", "Pharmacology", "Pharmacy", "Pathology", "Law", "Practice"]`.
- "note" : additional information about the question.


### images

The image filenames follow the format:  
`problem_id_{image_id}.png`


## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

YakugakuQA aims to provide a Japanese-language evaluation benchmark for assessing the domain knowledge of LLMs.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

All questions, answers and commentaries for the target years have been collected. The parsing process has been performed automatically.

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

All question, answers, and commentaries have been obtained from [yakugaku lab](https://yakugakulab.info/). All metadata has been obtained from the website of the Ministry of Health, Labour and Welfare. It should be noted that the original questions and answers are also sourced from materials published by the Ministry of Health, Labour and Welfare.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```
@misc{sukeda2025japaneselanguagemodelnew,
      title={A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP}, 
      author={Issey Sukeda and Takuro Fujii and Kosei Buma and Shunsuke Sasaki and Shinnosuke Ono},
      year={2025},
      eprint={2505.16661},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2505.16661}, 
}
```

## Contributions

Thanks to [@shinnosukeono](https://github.com/shinnosukeono) for adding this dataset.

## Acknowledgement

本データセットは、経済産業省及び国立研究開発法人新エネルギー・産業技術総合開発機構（NEDO）による生成AI開発力強化プロジェクト「GENIAC」により支援を受けた成果の一部である。