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

ACL ARR 2025 February Submission2393 Authors

Abstract: Existing benchmarks do not test Large Language 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 that 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 show that state-of-the-art LMM (e.g., OpenAI-o1) can correct their results through human feedback less than 50%. Our findings point to the need for methods that can enhance LMMs' capabilities to interpret and benefit from feedback.

Paper Type: Long

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: Human-AI Interaction, Multimodal Larger Language Model, Benchmark

Contribution Types: Model analysis & interpretability, Data analysis

Languages Studied: English

Submission Number: 2393