VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation

• URL: http://arxiv.org/abs/2602.21054v1
• Date: Tue, 24 Feb 2026 16:11:14 GMT
• Title: VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation
• Authors: Seongheon Park, Changdae Oh, Hyeong Kyu Choi, Xuefeng Du, Sharon Li,
• Abstract summary: Large Vision-Language Models (LVLMs) frequently hallucinate, limiting their safe deployment in real-world applications.<n>We propose VAUQ, a vision-aware uncertainty quantification framework for LVLM self-evaluation.<n>VAUQ explicitly measures how strongly a model's output depends on visual evidence.
• Score: 22.921677603408188
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Vision-Language Models (LVLMs) frequently hallucinate, limiting their safe deployment in real-world applications. Existing LLM self-evaluation methods rely on a model's ability to estimate the correctness of its own outputs, which can improve deployment reliability; however, they depend heavily on language priors and are therefore ill-suited for evaluating vision-conditioned predictions. We propose VAUQ, a vision-aware uncertainty quantification framework for LVLM self-evaluation that explicitly measures how strongly a model's output depends on visual evidence. VAUQ introduces the Image-Information Score (IS), which captures the reduction in predictive uncertainty attributable to visual input, and an unsupervised core-region masking strategy that amplifies the influence of salient regions. Combining predictive entropy with this core-masked IS yields a training-free scoring function that reliably reflects answer correctness. Comprehensive experiments show that VAUQ consistently outperforms existing self-evaluation methods across multiple datasets.

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