[2603.21693] Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain

Computer Science > Artificial Intelligence arXiv:2603.21693 (cs) [Submitted on 23 Mar 2026] Title:Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain Authors:Mohammad Asadi, Tahoura Nedaee, Jack W. O'Sullivan, Euan Ashley, Ehsan Adeli View a PDF of the paper titled Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain, by Mohammad Asadi and 4 other authors View PDF HTML (experimental) Abstract: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-probabilities: inconsistent token-level confidence and weak sensitivity to visual evidence. Based on this observation, we propose Confidence-Evidence Bayesian Gain (CEBaG), a deterministic hallucination detection method that requires no stochastic sampling, no external models, and no task-specific hyperparameters. CEBaG combines ...