Confidence Estimation for Financial Vision-Language Models in Chart and Document Understanding
LVLMs are increasingly used to read financial charts, tables, and documents, where a single misread figure can move a decision and the most authoritative-looking answer is sometimes one the model produced without reading the exhibit. The operational question is therefore trust, not accuracy: which answers can be acted on, and which escalated to a reviewer. We evaluate seven confidence estimators, three inference-only and four trained internal probes, across five open-weight LVLMs and four conditions from three financial visual question-answering benchmarks, one bilingual; every probe is trained only on natural images and applied to finance without adaptation, so the results measure out-of...
| Authors | Reza Khanmohammadi, Simerjot Kaur, Charese H. Smiley et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 August 2026 |
| Last updated at source | 6 August 2026 |
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