Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain
Multimodal large language models (MLLMs) have shown strong potential for medical Visual Question Answering (VQA), yet they remain prone to hallucinations, defined as generating responses that contradict the input image, posing serious risks in clinical settings. Current hallucination detection methods, such as Semantic Entropy (SE) and Vision-Amplified Semantic Entropy (VASE), require 10 to 20 stochastic generations per sample together with an external natural language inference model for semantic clustering, making them computationally expensive and difficult to deploy in practice. We observe that hallucinated responses exhibit a distinctive signature directly in the model's own log-prob...
| Authors | Mohammad Asadi, Tahoura Nedaee, Jack W. O'Sullivan et al. |
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| Primary category | cs.AI |
| Published | 23 March 2026 |
| Last updated at source | 17 July 2026 |
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