Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models (2025.emnlp-main)
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| Challenge: | Existing approaches to quantify uncertainty are limited in vision-language models . however, current models display notable miscalibration across diverse tasks and settings . |
| Approach: | They evaluate verbalized confidence in vision-language models using visual reasoning . they propose a prompting strategy that improves confidence alignment in multimodal settings . |
| Outcome: | The proposed method improves confidence alignment across multimodal settings. |
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