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arXiv Machine Learning By Tao Huang, Rui Wang, Xiaofei Liu, Yi Qin, Li Duan, Liping Jing

Detecting Misbehaviors of Large Vision-Language Models by Evidential Uncertainty Quantification

Read the original on arXiv Machine Learning →

The paper introduces Evidential Uncertainty Quantification (EUQ), a fine‑grained method for detecting misbehaviors in large vision‑language models (LVLMs). EUQ interprets model output features as supporting or opposing evidence and aggregates them using Evidence Theory to quantify internal conflict and ignorance in a single forward pass. Experiments on hallucinations, jailbreaks, adversarial attacks, and out‑of‑distribution failures show that EUQ outperforms strong baselines, linking hallucinations to high conflict and OOD failures to high ignorance, and provides layer‑wise dynamics for interpreting internal representations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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