Multimodal Hallucination Diagnosis in Vision-Language Models through Grounding, Self-Reflection, and Factuality Assessment across Visual Tasks
Keywords:
Vision-Language Models, Multimodal Hallucination, Visual Grounding, Self-Reflection, Factuality AssessmentAbstract
The rapid evolution of Vision-Language Models has precipitated unprecedented capabilities in multimodal understanding, reasoning, and generation. However, a persistent and critical vulnerability in these architectures is the manifestation of multimodal hallucinations, where models generate text that is ungrounded, factually incorrect, or contradictory to the provided visual stimuli. This paper introduces a comprehensive, multi-tiered diagnostic framework designed to detect, analyze, and mitigate such hallucinations across diverse visual tasks. By integrating fine-grained visual grounding, iterative self-reflection mechanisms, and rigorous factuality assessment protocols, the proposed system systematically evaluates model outputs against verifiable visual evidence and common-sense constraints. Grounding operates by explicitly linking generated entities to localized image regions, while self-reflection forces the model to critically re-evaluate its own preliminary hypotheses through structured prompting. The factuality assessment layer further references established knowledge boundaries to penalize epistemological overreach. Through extensive evaluation on benchmark datasets, this framework not only improves the interpretability of Vision-Language Models but also significantly reduces object and relational hallucinations. The experimental analysis demonstrates the efficacy of combining these three paradigms, offering a robust pathway toward developing highly reliable and trustworthy multimodal artificial intelligence systems tailored for high-stakes applications
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Copyright (c) 2026 Tess H. Peters, Niels Meijer
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