3 papers
cs.CL2026
Semantic Uncertainty Quantification Needs Factual Equivalence
Joseph Hoche, Quentin Guimard, Gianni Franchi
Semantic uncertainty quantification for large language models rests on a common template: sample several answers, measure how much they agree, and treat disagreement as uncertainty…
cs.CV2026
Leveraging Visual Signals for Robust Token-Level Uncertainty in Vision-Language Generation
Joseph Hoche, David Brellmann, Gianni Franchi
Uncertainty quantification (UQ) remains a critical challenge in Large Vision Language Models (LVLMs) for reliable predictions and real-world deployment. However, most existing meth…
cs.CV2025
Improving Semantic Uncertainty Quantification in LVLMs with Semantic Gaussian Processes
Joseph Hoche, Andrei Bursuc, David Brellmann +4
Large Vision-Language Models (LVLMs) often produce plausible but unreliable outputs, making robust uncertainty estimation essential. Recent work on semantic uncertainty estimates r…