@inproceedings{zhao-etal-2025-interfeedback,
    title = "{I}nter{F}eedback: Unveiling Interactive Intelligence of Large Multimodal Models with Human Feedback",
    author = "Zhao, Henry Hengyuan  and
      Pei, Wenqi  and
      Tao, Yifei  and
      Mei, Haiyang  and
      Shou, Mike Zheng",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.1383/",
    doi = "10.18653/v1/2025.findings-emnlp.1383",
    pages = "25381--25400",
    ISBN = "979-8-89176-335-7",
    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 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."
}