--- language: - ja license: other task_categories: - image-text-to-text size_categories: - 1K

HakushoBench: A Japanese Chart and Table VQA Benchmark from Governmental White Papers

| 🤗 Dataset  | 📄 Paper  | 🧑‍💻 Code  |
## Overview HakushoBench is a challenging Japanese chart and table VQA benchmark built from 33 governmental white papers. HakushoBench contains 2,053 images spanning over 10 image types, with manually annotated QA pairs, designed to assess deep and holistic understanding of charts and tables, rather than local visual cues alone. ## Construction Pipeline of HakushoBench HakushoBench is constructed through the following three stages: 1. **Chart & Table Image Collection**: Chart and table images are collected from 33 Japanese governmental white papers and filtered to 5,903 candidate images. 2. **QA Annotation**: Annotators create one challenging QA pair for each image. 3. **QA Verification**: All annotations undergo independent verification, resulting in a final benchmark of 2,053 manually validated VQA pairs.
Construction pipeline of HakushoBench
Construction pipeline of HakushoBench.
## Domain Coverage HakushoBench is constructed from Japanese governmental white papers spanning diverse domains, including security, economics, society. This broad coverage enables comprehensive evaluation of chart and table understanding across a wide range of real-world topics.
Topic distribution
Distribution of QA pairs in HakushoBench.
## Evaluation You can evaluate models on HakushoBench using [simple-evals-mm](https://github.com/llm-jp/simple-evals-mm) The figure below presents evaluation results on HakushoBench. Despite recent advances in multimodal models, a substantial performance gap remains between the strongest proprietary model, Gemini 3 Pro, and the best open-weight model, Qwen3-VL-8B, suggesting considerable room for improvement in open-weight models on chart and table understanding.
evaluation results
Performance of each model on HakushoBench.
## How to Use Due to copyright restrictions, the images in HakushoBench are not distributed through Hugging Face and are instead hosted on a domestic server in Japan. To obtain the images required for evaluation, clone the GitLab repository: ```bash $ git clone https://gitlab.llm-jp.nii.ac.jp/datasets/hakushobench ``` After downloading the dataset locally, you can load it using the 🤗 Datasets library: ```python from datasets import load_dataset ds = load_dataset("hakushobench/data", split="test") print(ds) Dataset({ features: ['original_id', 'image', 'category', 'question', 'answer', 'question_type', 'image_url', 'image_path', 'image_type'], num_rows: 2053 }) ``` ## License HakushoBench (excluding images) is licensed under the Apache License 2.0. Images are provided solely for use within the scope permitted by Article 30-4 of the Japanese Copyright Act. Copyright for each image remains with its respective copyright holder. ## Citation If you find HakushoBench useful, please consider citing our work: ```bibtex @misc{sugiura2026hakushobenchjapanesecharttable, title={HakushoBench: A Japanese Chart and Table VQA Benchmark from Governmental White Papers}, author={Issa Sugiura and Shuhei Kurita and Yusuke Oda and Naoaki Okazaki}, year={2026}, eprint={2606.01132}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2606.01132}, } ```