AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models

[🍎 Project Page] [📖 arXiv Paper] [📊 Dataset]

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🔥 News

• 2024.06.14 🌟 We released AlignMMBench, a comprehensive alignment benchmark for vision language models!

👀 Introduce to AlignMMBench

AlignMMBench a multimodal alignment benchmark that encompasses both single-turn and multi-turn dialogue scenarios. It includes three categories and thirteen capability tasks, with a total of 4,978 question-answer pairs.

Features

1. High-Quality Annotations: Reliable benchmark with meticulous human annotation and multi-stage quality control processes.

2. Self Critic: To improve the controllability of alignment evaluation, we introduce the CritiqueVLM, a ChatGLM3-6B based evaluator that has been rule-calibrated and carefully finetuned. With human judgements, its evaluation consistency surpasses that of GPT-4.

3. Diverse Data: Three categories and thirteen capability tasks, including both single-turn and multi-turn dialogue scenarios.

📈 Results

License

The use of the dataset and the original videos is governed by the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license, as detailed in the LICENSE.

If you believe that any content in this dataset infringes on your rights, please contact us at wenmeng.yu@aminer.cn to request its removal.

Citation

If you find our work helpful for your research, please consider citing our work.

@misc{wu2024alignmmbench,
      title={AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models}, 
      author={Yuhang Wu and Wenmeng Yu and Yean Cheng and Yan Wang and Xiaohan Zhang and Jiazheng Xu and Ming Ding and Yuxiao Dong},
      year={2024},
      eprint={2406.09295},
      archivePrefix={arXiv}
}