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Australian Institute for Machine Learning, Adelaide University
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Overconfident visual embeddings can mask true uncertainty, but Visual Semantic Entropy reveals the hidden complexity of visual ambiguity in vision-language models.
Hallucinated tokens in LVLMs betray themselves through diffuse attention patterns and a failure to semantically align with any specific image region, enabling highly accurate detection.