Robust Multimodal Large Language Models Against Modality Conflict
Despite the impressive capabilities of multimodal 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
conflicts in inputs from different modalities that place MLLMs in a dilemma and
directly lead to hallucinations. We formally define the modality conflict and
construct a dataset named Multimodal Modality Conflict (MMMC) to simulate this
phenomenon in vision-language tasks. Three methods 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 demerits 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 performance. Our
work sheds light on the unnoticed modality conflict that leads to
hallucinations and provides more insights into the robustness of MLLMs.
68707d75c8391850d6097823
Zongmeng Zhang
claimed_verified
2025-07-11T08:00:24.241Z
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/avatars/ae39f9b84f9379423fa3a8509fbcc94e.svg
Zongmeng Zhang
user
ustc-zhangzm
68707d75c8391850d6097824
Wengang Zhou
68707d75c8391850d6097825
Jie Zhao
68707d75c8391850d6097826
Houqiang Li
Robust Multimodal Large Language Models Against Modality Conflict
Despite the impressive capabilities of multimodal 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
conflicts in inputs from different modalities that place MLLMs in a dilemma and
directly lead to hallucinations. We formally define the modality conflict and
construct a dataset named Multimodal Modality Conflict (MMMC) to simulate this
phenomenon in vision-language tasks. Three methods 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 demerits 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 performance. Our
work sheds light on the unnoticed modality conflict that leads to
hallucinations and provides more insights into the robustness of MLLMs.
Modality conflict in multimodal large language models leads to hallucinations, and reinforcement learning is found to be the most effective method to mitigate this issue.
multimodal large language models hallucinations modality conflict prompt engineering supervised fine-tuning reinforcement learning MMMC dataset