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Mitigating Multimodal LLMs Hallucinations via Relevance Propagation at Inference Time
Multimodal large language models (MLLMs) achieve strong performance on vision- and audio-language tasks, yet can generate responses that conflict with the given visual or auditory inputs, a problem known as multimodal hallucinations. Prior work suggests that this occurs when models rely more on text
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