Language and Vision-Language Models (LLMs/VLMs) have revolutionized the field
of AI by their ability to generate human-like text and understand images, but
ensuring their reliability is crucial. This paper aims to evaluate the ability
of LLMs (GPT4, GPT-3.5, LLaMA2, and PaLM 2) and VLMs (GPT4V and Gemini Pro
Vision) to estimate their verbalized uncertainty via prompting. We propose the
new Japanese Uncertain Scenes (JUS) dataset, aimed at testing VLM capabilities
via difficult queries and object counting, and the Net Calibration Error (NCE)
to measure direction of miscalibration. Results show that both LLMs and VLMs
have a high calibration error and are overconfident most of the time,
indicating a poor capability for uncertainty estimation. Additionally we
develop prompts for regression tasks, and we show that VLMs have poor
calibration when producing mean/standard deviation and 95% confidence
intervals.

该论文评估了语言和视觉 - 语言模型的可靠性，并提出了新的日本不确定场景数据集以及测量校准误差的方法。结果表明，语言和视觉 - 语言模型都存在较高的校准误差，并且大部分时间都表现出过度自信，说明其对不确定性估计的能力较弱。此外，研究还发展了回归任务的提示方法，并证明视觉 - 语言模型在生成均值 / 标准差和 95% 置信区间时存在校准不良。