LLaVA-Critic: Learning to Evaluate Multimodal Models

• URL: http://arxiv.org/abs/2410.02712v1
• Date: Thu, 3 Oct 2024 17:36:33 GMT
• Title: LLaVA-Critic: Learning to Evaluate Multimodal Models
• Authors: Tianyi Xiong, Xiyao Wang, Dong Guo, Qinghao Ye, Haoqi Fan, Quanquan Gu, Heng Huang, Chunyuan Li,
• Abstract summary: We introduce LLaVA-Critic, the first open-source large multimodal model (LMM) designed as a generalist evaluator. LLaVA-Critic is trained using a high-quality critic instruction-following dataset that incorporates diverse evaluation criteria and scenarios.
• Score: 110.06665155812162
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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