General-Level/General-Bench-Closeset

On Path to Multimodal Generalist: Levels and Benchmarks

[📖 Project] [🏆 Leaderboard] [📄 Paper] [🤗 Dataset-HF] [📝 Dataset-Github]

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We divide our benchmark into two settings: open and closed.

This is the Closed benchmark of Generalist-Bench, where we release only the question annotations—without ground-truth answers—for all datasets.

You can follow the detailed usage instructions to submit the resuls generate by your own model.

Final results will be updated on the 🏆 Leaderboard.

If you’d like to train or evaluate your model with access to the full answers, please check out the 👉 open benchmark, where all ground-truth annotations are provided.

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📕 Table of Contents

• ✨ File Origanization Structure
• 🍟 Usage
• 🌐 General-Bench
  • 🍕 Capabilities and Domians Distribution
• 🖼️ Image Task Taxonomy
• 📽️ Video Task Taxonomy
• 📞 Audio Task Taxonomy
• 💎 3D Task Taxonomy
• 📚 Language Task Taxonomy

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✨✨✨ File Origanization Structure

Here is the organization structure of the file system:

General-Bench
├── Image
│   ├── comprehension
│   │   ├── Bird-Detection
│   │   │   ├── annotation.json
│   │   │   └── images
│   │   │       └── Acadian_Flycatcher_0070_29150.jpg
│   │   ├── Bottle-Anomaly-Detection
│   │   │   ├── annotation.json
│   │   │   └── images
│   │   └── ...
│   └── generation
│       └── Layout-to-Face-Image-Generation
│           ├── annotation.json
│           └── images
│           └── ...
├── Video
│   ├── comprehension
│   │   └── Human-Object-Interaction-Video-Captioning
│   │       ├── annotation.json
│   │       └── videos
│   │       └── ...
│   └── generation
│       └── Scene-Image-to-Video-Generation
│           ├── annotation.json
│           └── videos
│           └── ...
├── 3d
│   ├── comprehension
│   │   └── 3D-Furniture-Classification
│   │       ├── annotation.json
│   │       └── pointclouds
│   │       └── ...
│   └── generation
│       └── Text-to-3D-Living-and-Arts-Point-Cloud-Generation
│           ├── annotation.json
│           └── pointclouds
│           └── ...
├── Audio
│   ├── comprehension
│   │   └── Accent-Classification
│   │       ├── annotation.json
│   │       └── audios
│   │       └── ...
│   └── generation
│       └── Video-To-Audio
│           ├── annotation.json
│           └── audios
│           └── ...
├── NLP
│   ├── History-Question-Answering
│   │   └── annotation.json
│   ├── Abstractive-Summarization
│   │   └── annotation.json
│   └── ...

An illustrative example of file formats:

🍟🍟🍟 Usage

Please download all the files in this repository. We also provide overview.json, which is an example of the format of our dataset.

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🌐🌐🌐 General-Bench

A companion massive multimodal benchmark dataset, encompasses a broader spectrum of skills, modalities, formats, and capabilities, including over 700 tasks and 325K instances.

Overview of General-Bench, which covers 145 skills for more than 700 tasks with over 325,800 samples under comprehension and generation categories in various modalities

🍕🍕🍕 Capabilities and Domians Distribution

Distribution of various capabilities evaluated in General-Bench.

Distribution of various domains and disciplines covered by General-Bench.

🖼️ Image Task Taxonomy

Taxonomy and hierarchy of data in terms of Image modality.

📽️ Video Task Taxonomy

Taxonomy and hierarchy of data in terms of Video modality.

📞 Audio Task Taxonomy

Taxonomy and hierarchy of data in terms of Audio modality.

💎 3D Task Taxonomy

Taxonomy and hierarchy of data in terms of 3D modality.

📚 Language Task Taxonomy

Taxonomy and hierarchy of data in terms of Language modality.

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🚩 Citation

If you find our benchmark useful in your research, please kindly consider citing us:

@article{generalist2025,
  title={On Path to Multimodal Generalist: Levels and Benchmarks},
  author={Hao Fei, Yuan Zhou, Juncheng Li, Xiangtai Li, Qingshan Xu, Bobo Li, Shengqiong Wu, Yaoting Wang, Junbao Zhou, Jiahao Meng, Qingyu Shi, Zhiyuan Zhou, Liangtao Shi, Minghe Gao, Daoan Zhang, Zhiqi Ge, Siliang Tang, Kaihang Pan, Yaobo Ye, Haobo Yuan, Tao Zhang, Weiming Wu, Tianjie Ju, Zixiang Meng, Shilin Xu, Liyu Jia, Wentao Hu, Meng Luo, Jiebo Luo, Tat-Seng Chua, Hanwang Zhang, Shuicheng YAN},
  journal={arXiv},
  year={2025}
}