AI RESEARCH
Improving Semantic Uncertainty Quantification in LVLMs with Semantic Gaussian Processes
arXiv CS.CV
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ArXi:2512.14177v2 Announce Type: replace Large Vision-Language Models (LVLMs) often produce plausible but unreliable outputs, making robust uncertainty estimation essential. Recent work on semantic uncertainty estimates relies on external models to cluster multiple sampled responses and measure their semantic consistency. However, these clustering methods are often fragile, highly sensitive to minor phrasing variations, and can incorrectly group or separate semantically similar answers, leading to unreliable uncertainty estimates.