ICML 2025

Robust Multimodal Large Language Models Against Modality Conflict
Zongmeng Zhang 1 Wengang Zhou 2 Jie Zhao 3 Houqiang Li 2
Abstract
Despite the impressive capabilities of multi-
modal large language models (MLLMs) in vision-
language tasks, they are prone to hallucinations in
real-world scenarios. This paper investigates the
hallucination phenomenon in MLLMs from the
perspective of modality conflict. Unlike existing
works focusing on the conflicts between model
responses and inputs, we study the inherent con-
flicts in inputs from different modalities that place
MLLMs in a dilemma and directly lead to hallu-
cinations. We formally define the modality con-
flict and construct a dataset named Multimodal
Modality Conflict (MMMC) to simulate this phe-
nomenon in vision-language tasks. Three meth-
ods based on prompt engineering, supervised fine-
tuning, and reinforcement learning are proposed
to alleviate the hallucination caused by modality
conflict. Extensive experiments are conducted on
the MMMC dataset to analyze the merits and de-
merits of these methods. Our results show that
the reinforcement learning method achieves the
best performance in mitigating the hallucination
under modality conflict, while the supervised fine-
tuning method shows promising and stable per-
formance. Our work sheds light on the unno-
ticed modality conflict that leads to hallucinations
and provides more insights into the robustness
of MLLMs. The code and dataset are available
at https://github.com/zmzhang2000/
MMMC.
1School of Artificial Intelligence and Data Science, University
of Science and Technology of China 2Department of Electronic
Engineering and Information Science, University of Science and
Technology of China 3Huawei Technologies Co., Ltd.. Correspon-
dence to: Wengang Zhou <zhwg@ustc.edu.cn>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025
by the author(s).
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