  Recently, it has been recognized that large language models demonstrate high
performance on various intellectual tasks. However, few studies have
investigated alignment with humans in behaviors that involve sensibility, such
as aesthetic evaluation. This study investigates the performance of GPT-4 with
Vision, a state-of-the-art language model that can handle image input, on the
task of aesthetic evaluation of images. We employ two tasks, prediction of the
average evaluation values of a group and an individual's evaluation values. We
investigate the performance of GPT-4 with Vision by exploring prompts and
analyzing prediction behaviors. Experimental results reveal GPT-4 with Vision's
superior performance in predicting aesthetic evaluations and the nature of
different responses to beauty and ugliness. Finally, we discuss developing an
AI system for aesthetic evaluation based on scientific knowledge of the human
perception of beauty, employing agent technologies that integrate traditional
deep learning models with large language models.
