InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback

• URL: http://arxiv.org/abs/2502.15027v2
• Date: Sun, 09 Mar 2025 01:07:59 GMT
• Title: InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models via Human Feedback
• Authors: Henry Hengyuan Zhao, Wenqi Pei, Yifei Tao, Haiyang Mei, Mike Zheng Shou,
• Abstract summary: Existing benchmarks do not test Large Multimodal Models (LMMs) on their interactive intelligence with human users.<n>We design InterFeedback, an interactive framework, which can be applied to any LMM and dataset to assess this ability autonomously.<n>We present InterFeedback-Human, a dataset of 120 cases designed for manually testing interactive performance in leading models.
• Score: 16.031192986950465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing benchmarks do not test Large Multimodal Models (LMMs) on their interactive intelligence with human users, which is vital for developing general-purpose AI assistants. We design InterFeedback, an interactive framework, which can be applied to any LMM and dataset to assess this ability autonomously. On top of this, we introduce InterFeedback-Bench which evaluates interactive intelligence using two representative datasets, MMMU-Pro and MathVerse, to test 10 different open-source LMMs. Additionally, we present InterFeedback-Human, a newly collected dataset of 120 cases designed for manually testing interactive performance in leading models such as OpenAI-o1 and Claude-3.5-Sonnet. Our evaluation results indicate that even the state-of-the-art LMM, OpenAI-o1, struggles to refine its responses based on human feedback, achieving an average score of less than 50%. Our findings point to the need for methods that can enhance LMMs' capabilities to interpret and benefit from feedback.

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