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---
dataset_info:
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: train
num_bytes: 157153160.0
num_examples: 129
download_size: 157133331
dataset_size: 157153160.0
configs:
- config_name: default
data_files:
- split: train
path: hard_data/train-*
---
# 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](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
`data/`
This directory contains all 500 samples of VisualSimpleQA stored in parquet files.
`hard_data/`
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 [email protected]. 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](https://creativecommons.org/licenses/by/4.0/)
- Images: Subject to custom terms (see Disclaimer above).
## Citation
**BibTeX:**
```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}
}
``` |