---
task_categories:
- multiple-choice
- question-answering
- visual-question-answering
language:
- en
size_categories:
- 1K<n<10K
configs:
- config_name: val
  data_files:
  - split: val
    path: "mmstar.parquet"
dataset_info:
  - config_name: val
    features:
      - name: index
        dtype: int64
      - name: question
        dtype: string
      - name: image
        dtype: image
      - name: answer
        dtype: string
      - name: category
        dtype: string
      - name: l2_category
        dtype: string
      - name: meta_info
        struct:
        - name: source
          dtype: string
        - name: split
          dtype: string
        - name: image_path
          dtype: string
    splits:
      - name: val
        num_bytes: 44831593
        num_examples: 1500
---

# MMStar (Are We on the Right Way for Evaluating Large Vision-Language Models?)

[**🌐 Homepage**](https://mmstar-benchmark.github.io/) | [**🤗 Dataset**](https://huggingface.co/datasets/Lin-Chen/MMStar) | [**🤗 Paper**](https://huggingface.co/papers/2403.20330) | [**📖 arXiv**](https://arxiv.org/pdf/2403.20330.pdf) | [**GitHub**](https://github.com/MMStar-Benchmark/MMStar)

## Dataset Details

As shown in the figure below, existing benchmarks lack consideration of the vision dependency of evaluation samples and potential data leakage from LLMs' and LVLMs' training data.

<p align="center">
    <img src="https://raw.githubusercontent.com/MMStar-Benchmark/MMStar/main/resources/4_case_in_1.png" width="80%"> <br>
</p>

Therefore, we introduce MMStar: an elite vision-indispensible multi-modal benchmark, aiming to ensure each curated sample exhibits **visual dependency**, **minimal data leakage**, and **requires advanced multi-modal capabilities**.

🎯 **We have released a full set comprising 1500 offline-evaluating samples.** After applying the coarse filter process and manual review, we narrow down from a total of 22,401 samples to 11,607 candidate samples and finally select 1,500 high-quality samples to construct our MMStar benchmark.

<p align="center">
    <img src="https://raw.githubusercontent.com/MMStar-Benchmark/MMStar/main/resources/data_source.png" width="80%"> <br>
</p>

In MMStar, we display **6 core capabilities** in the inner ring, with **18 detailed axes** presented in the outer ring. The middle ring showcases the number of samples for each detailed dimension. Each core capability contains a meticulously **balanced 250 samples**. We further ensure a relatively even distribution across the 18 detailed axes.

<p align="center">
    <img src="https://raw.githubusercontent.com/MMStar-Benchmark/MMStar/main/resources/mmstar.png" width="60%"> <br>
</p>

## 🏆 Mini-Leaderboard
We show a mini-leaderboard here and please find more information in our paper or [homepage](https://mmstar-benchmark.github.io/).

| Model                      | Acc. | MG ⬆ | ML ⬇ |
|----------------------------|:---------:|:------------:|:------------:|
| GPT4V (high)| **57.1**  |      **43.6**       | 1.3 |
| InternLM-Xcomposer2|  55.4 |      28.1       | 7.5|
| LLaVA-Next-34B |52.1|29.4|2.4|
|GPT4V (low)|46.1|32.6|1.3|
|InternVL-Chat-v1.2|43.7|32.6|**0.0**|
|GeminiPro-Vision|42.6|27.4|**0.0**|
|Sphinx-X-MoE|38.9|14.8|1.0|
|Monkey-Chat|38.3|13.5|17.6|
|Yi-VL-6B|37.9|15.6|**0.0**|
|Qwen-VL-Chat|37.5|23.9|**0.0**|
|Deepseek-VL-7B|37.1|15.7|**0.0**|
|CogVLM-Chat|36.5|14.9|**0.0**|
|Yi-VL-34B|36.1|18.8|**0.0**|
|TinyLLaVA|36.0|16.4|7.6|
|ShareGPT4V-7B|33.0|11.9|**0.0**|
|LLaVA-1.5-13B|32.8|13.9|**0.0**|
|LLaVA-1.5-7B|30.3|10.7|**0.0**|
|Random Choice|24.6|-|-|

## 📧 Contact

- [Lin Chen](https://lin-chen.site/): chlin@mail.ustc.edu.cn
- [Jinsong Li](https://li-jinsong.github.io/): lijingsong@pjlab.org.cn

## ✒️ Citation

If you find our work helpful for your research, please consider giving a star ⭐ and citation 📝
```bibtex
@article{chen2024right,
    title={Are We on the Right Way for Evaluating Large Vision-Language Models?}, 
    author={Chen, Lin and Li, Jinsong and Dong, Xiaoyi and Zhang, Pan and Zang, Yuhang and Chen, Zehui and Duan, Haodong and Wang, Jiaqi and Qiao, Yu and Lin, Dahua and Zhao, Feng},
    journal={arXiv preprint arXiv:2403.20330},
    year={2024}
}
```