视觉语言模型在图像受损时会高估答案可信度，本文提出验证其不确定性估计能力的方法。
Know What You do Not Know: Verbalized Uncertainty Estimation Robustness on Corrupted Images in Vision-Language Models
- 测试三种顶级视觉语言模型在损坏图像上的不确定性判断能力
- 图像越严重损坏，模型越无法准确评估自身答案可靠性
- 发现模型普遍存在过度自信问题，适合关注模型可信度的研究者阅读
为充分发挥大语言模型（LLMs）的潜力，必须了解其回答的不确定性，即模型对自己答案正确性的把握程度。错误的不确定性估计会导致模型对错误答案过于自信，从而削弱用户信任。尽管针对纯文本输入输出的语言模型已有大量研究，但随着视觉能力被加入模型，视觉语言模型（VLMs）的不确定性估计仍缺乏进展。本文在受损图像数据上测试了三种最先进的VLMs，发现图像损坏程度越高，模型越难以准确估计自身不确定性；且多数实验中模型表现出明显过度自信，这揭示了当前VLMs在鲁棒性评估方面的关键缺陷。
原文摘要 · Abstract (English)
To leverage the full potential of Large Language Models (LLMs) it is crucial to have some information on their answers' uncertainty. This means that the model has to be able to quantify how certain it is in the correctness of a given response. Bad uncertainty estimates can lead to overconfident wrong answers undermining trust in these models. Quite a lot of research has been done on language models that work with text inputs and provide text outputs. Still, since the visual capabilities have been added to these models recently, there has not been much progress on the uncertainty of Visual Language Models (VLMs). We tested three state-of-the-art VLMs on corrupted image data. We found that the severity of the corruption negatively impacted the models' ability to estimate their uncertainty and the models also showed overconfidence in most of the experiments.
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