README.md

metadata

dataset_info:
  - config_name: VisualSimpleQA
    features:
      - name: id
        dtype: int64
      - name: multimodal_question
        dtype: string
      - name: answer
        dtype: string
      - name: rationale
        dtype: string
      - name: text_only_question
        dtype: string
      - name: image_source
        dtype: string
      - name: evidence
        dtype: string
      - name: resolution
        dtype: string
      - name: proportion_of_roi
        dtype: string
      - name: category
        dtype: string
      - name: text_in_image
        dtype: string
      - name: rationale_granularity
        dtype: string
      - name: image
        dtype: image
      - name: cropped_image
        dtype: image
    splits:
      - name: test
        num_bytes: 515536503
        num_examples: 500
    download_size: 515227519
    dataset_size: 515536503
  - config_name: VisualSimpleQA_hard
    features:
      - name: id
        dtype: int64
      - name: multimodal_question
        dtype: string
      - name: answer
        dtype: string
      - name: rationale
        dtype: string
      - name: text_only_question
        dtype: string
      - name: image_source
        dtype: string
      - name: evidence
        dtype: string
      - name: resolution
        dtype: string
      - name: proportion_of_roi
        dtype: string
      - name: category
        dtype: string
      - name: text_in_image
        dtype: string
      - name: rationale_granularity
        dtype: string
      - name: image
        dtype: image
      - name: cropped_image
        dtype: image
    splits:
      - name: test
        num_bytes: 157153160
        num_examples: 129
    download_size: 157133331
    dataset_size: 157153160
configs:
  - config_name: VisualSimpleQA
    data_files:
      - split: test
        path: VisualSimpleQA/test-*
  - config_name: VisualSimpleQA_hard
    data_files:
      - split: test
        path: VisualSimpleQA_hard/test-*

VisualSimpleQA

Introduction

VisualSimpleQA is a multimodal fact-seeking benchmark with two key features. First, it enables streamlined and decoupled evaluation of LVLMs in visual and linguistic modalities. Second, it incorporates well-defined difficulty criteria to guide human annotation and facilitates the extraction of a challenging subset, VisualSimpleQA-hard. Experiments on 15 LVLMs show that even state-of-the-art models such as GPT-4o achieve merely 60%+ correctness in multimodal fact-seeking QA on VisualSimpleQA and 30%+ on VisualSimpleQA-hard. Furthermore, the decoupled evaluation based on this benchmark across different models highlights substantial opportunities for improvement in both visual and linguistic modules. The dataset reviewer above illustrates 129 samples from VisualSimpleQA-hard.

arXiv: https://arxiv.org/pdf/2503.06492

Data Example:

  {'id': 369,
   'multimodal_question': 'Which institution did the creator of this cartoon duck donate her natural science-related paintings to?',
   'answer': 'The Armitt Museum, Gallery, Library', 'rationale': 'Jemima Puddle-Duck',
   'text_only_question': 'Which institution did the creator of Jemima Puddle-Duck donate her natural science-related paintings to?',
   'image_source': 'https://www.gutenberg.org/files/14814/14814-h/images/15-tb.jpg',
   'evidence': 'https://www.armitt.com/beatrix-potter-exhibition/\nhttps://en.wikipedia.org/wiki/Beatrix_Potter',
   'resolution': '400x360',
   'proportion_of_roi': '0.2232',
   'category': 'research and education',
   'text_in_image': 'absence',
   'rationale_granularity': 'fine-grained',
   'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=400x360 at 0x7FE82C270D70>,
   'cropped_image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=164x196 at 0x7FE82C329550>}

File Structure

VisualSimpleQA/ This directory contains all 500 samples of VisualSimpleQA stored in parquet files.

VisualSimpleQA_hard/ This directory contains 129 VisualSimpleQA-hard samples stored in a parquet file. These samples are selected based on well-defined criteria to ensure they represent more challenging cases from VisualSimpleQA.

Disclaimer

This dataset contains images collected from various sources. The authors do NOT claim ownership or copyright over the images. The images may be subject to third-party rights, and users are solely responsible for verifying the legal status of any content before use.

• Intended Use: The images are provided for ​non-commercial research purposes only.

• Redistribution Prohibition: You may NOT redistribute or modify the images without permission from original rights holders.

• Reporting Violations: If you encounter any sample potentially breaching copyright or licensing rules, contact us at yanlingwang777@gmail.com. Verified violations will be removed promptly.

The authors disclaim all liability for copyright infringement or misuse arising from the use of this dataset. Users assume full legal responsibility for their actions.

License

• Text Data: Licensed under CC-BY-4.0
• Images: Subject to custom terms (see Disclaimer above).

Citation

BibTeX:

@article{wang2025visualsimpleqa,
  title={VisualSimpleQA: A Benchmark for Decoupled Evaluation of Large Vision-Language Models in Fact-Seeking Question Answering},
  author={Yanling Wang and Yihan Zhao and Xiaodong Chen and Shasha Guo and Lixin Liu and Haoyang Li and Yong Xiao and Jing Zhang and Qi Li and Ke Xu},
  journal={arXiv preprint arXiv: 2503.06492},
  year={2025}
}