  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.
