Seeing is Believing, but How Much? A Comprehensive Analysis of
  Verbalized Calibration in Vision-Language Models
Uncertainty quantification is essential for assessing the reliability and
trustworthiness of modern AI systems. Among existing approaches, verbalized
uncertainty, where models express their confidence through natural language,
has emerged as a lightweight and interpretable solution in large language
models (LLMs). However, its effectiveness in vision-language models (VLMs)
remains insufficiently studied. In this work, we conduct a comprehensive
evaluation of verbalized confidence in VLMs, spanning three model categories,
four task domains, and three evaluation scenarios. Our results show that
current VLMs often display notable miscalibration across diverse tasks and
settings. Notably, visual reasoning models (i.e., thinking with images)
consistently exhibit better calibration, suggesting that modality-specific
reasoning is critical for reliable uncertainty estimation. To further address
calibration challenges, we introduce Visual Confidence-Aware Prompting, a
two-stage prompting strategy that improves confidence alignment in multimodal
settings. Overall, our study highlights the inherent miscalibration in VLMs
across modalities. More broadly, our findings underscore the fundamental
importance of modality alignment and model faithfulness in advancing reliable
multimodal systems.
68365bc843cbbe13e0fabdad
Weihao Xuan
claimed_verified
2025-07-22T07:54:33.552Z
65b8909c89eb3dfbe8d26780
https://cdn-avatars.huggingface.co/v1/production/uploads/65b8909c89eb3dfbe8d26780/aJExXsCN9oQOdd9bsQOEO.jpeg
Weihao XUAN
user
weihao1115
68365bc843cbbe13e0fabdae
Qingcheng Zeng
68365bc843cbbe13e0fabdaf
Heli Qi
68365bc843cbbe13e0fabdb0
Junjue Wang
68365bc843cbbe13e0fabdb1
Naoto Yokoya
Seeing is Believing, but How Much? A Comprehensive Analysis of
  Verbalized Calibration in Vision-Language Models
Uncertainty quantification is essential for assessing the reliability and
trustworthiness of modern AI systems. Among existing approaches, verbalized
uncertainty, where models express their confidence through natural language,
has emerged as a lightweight and interpretable solution in large language
models (LLMs). However, its effectiveness in vision-language models (VLMs)
remains insufficiently studied. In this work, we conduct a comprehensive
evaluation of verbalized confidence in VLMs, spanning three model categories,
four task domains, and three evaluation scenarios. Our results show that
current VLMs often display notable miscalibration across diverse tasks and
settings. Notably, visual reasoning models (i.e., thinking with images)
consistently exhibit better calibration, suggesting that modality-specific
reasoning is critical for reliable uncertainty estimation. To further address
calibration challenges, we introduce Visual Confidence-Aware Prompting, a
two-stage prompting strategy that improves confidence alignment in multimodal
settings. Overall, our study highlights the inherent miscalibration in VLMs
across modalities. More broadly, our findings underscore the fundamental
importance of modality alignment and model faithfulness in advancing reliable
multimodal systems.
The study evaluates verbalized confidence in vision-language models, revealing miscalibration across tasks and suggesting that modality-specific reasoning and Visual Confidence-Aware Prompting can improve reliability.
vision-language models verbalized uncertainty natural language visual reasoning models multimodal settings confidence calibration Visual Confidence-Aware Prompting modality alignment model faithfulness