LLaVA-Critic: Learning to Evaluate Multimodal Models
<|reference_start|>LLaVA-Critic: Learning to Evaluate Multimodal Models: We introduce LLaVA-Critic, the first open-source large multimodal model (LMM) designed as a generalist evaluator to assess performance across a wide range of multimodal tasks. LLaVA-Critic is trained using a high-quality critic instruction-following dataset that incorporates diverse evaluation criteria and scenarios. Our experiments demonstrate the model's effectiveness in two key areas: (1) LMM-as-a-Judge, where LLaVA-Critic provides reliable evaluation scores, performing on par with or surpassing GPT models on multiple evaluation benchmarks; and (2) Preference Learning, where it generates reward signals for preference learning, enhancing model alignment capabilities. This work underscores the potential of open-source LMMs in self-critique and evaluation, setting the stage for future research into scalable, superhuman alignment feedback mechanisms for LMMs.<|reference_end|>
@article{xiong2024llava-critic:,
    title={LLaVA-Critic: Learning to Evaluate Multimodal Models},
    author={Tianyi Xiong, Xiyao Wang, Dong Guo, Qinghao Ye, Haoqi Fan, Quanquan
  Gu, Heng Huang, Chunyuan Li},
    journal={arXiv preprint arXiv:2410.02712},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02712},
    primaryClass={cs.CV cs.CL}
}