AI RESEARCH
VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation
arXiv CS.CL
•
ArXi:2602.21054v2 Announce Type: replace-cross 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.