.
├── README.md
├── config
├── document
├── figure
├── process
├── requirements.txt
├── run_script.sh
└── tools

config: Including prompt to use and parameters to set, etc.

document: model's final performance, examiner priority, and position bias.

figure: figures used in paper

process: code of AutoBench-V

tools: Some common tools, such as image base64 conversion, data visualization and so on.

run_script.sh: api to use.

pip -r install requirements.txt
./run_script.sh
python pipeline.py

Remember to change parameters: user_input and generate_type when run pipeline.py.

five options for user_input:

• basic_understanding
• spatial_understanding
• semantic_understanding
• reasoning_capacity
• atmospheric_understanding

For a complete pipeline, you only need to use 7 kinds for generate_type in order:

• aspect: generate aspects
• prompts: generate image descriptions
• images: generate images based on description
• alignment: test the alignment of images and descriptions via VQA
• questions: generate questions to test LVLMs
• adjust: adjust the option distribution of questions
• answers: answer questions and score

@misc{bao2025autobenchvlargevisionlanguagemodels,
      title={AutoBench-V: Can Large Vision-Language Models Benchmark Themselves?}, 
      author={Han Bao and Yue Huang and Yanbo Wang and Jiayi Ye and Xiangqi Wang and Xiuying Chen and Yue Zhao and Tianyi Zhou and Mohamed Elhoseiny and Xiangliang Zhang},
      year={2025},
      eprint={2410.21259},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2410.21259}, 
}

If you have any questions, suggestions, or would like to collaborate, please feel free to reach out to us via email at hbao@nd.edu