Leveraging Visual Signals for Robust Token-Level Uncertainty in Vision-Language Generation
Uncertainty quantification (UQ) remains a critical challenge in Large Vision Language Models (LVLMs) for reliable predictions and real-world deployment. However, most existing methods are adapted from the LLM literature and primarily focus on the language modality, leaving the contribution of visual information to LVLM uncertainty largely underexplored. In this paper, we investigate how LVLMs process visual information and whether this process can be used to improve uncertainty estimation. By analyzing hidden representations after the integration of visual features during the generation process, we observe that high-confidence predictions rely more heavily on visual content than uncertain...
| Authors | Joseph Hoche, David Brellmann, Gianni Franchi |
|---|---|
| Primary category | cs.CV |
| Published | 26 May 2026 |
| Last updated at source | 9 September 2026 |
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