@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models with Human Feedback
%A Zhao, Henry Hengyuan
%A Pei, Wenqi
%A Tao, Yifei
%A Mei, Haiyang
%A Shou, Mike Zheng
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F zhao-etal-2025-interfeedback
%X 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.
%R 10.18653/v1/2025.findings-emnlp.1383
%U https://aclanthology.org/2025.findings-emnlp.1383/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1383
%P 25381-25400
Markdown (Informal)
[InterFeedback: Unveiling Interactive Intelligence of Large Multimodal Models with Human Feedback](https://aclanthology.org/2025.findings-emnlp.1383/) (Zhao et al., Findings 2025)
ACL