Harnessing the Power of LLMs for Image Aesthetics Assessment Through Semantic and Contextual understanding

Human aesthetic appreciation of images is shaped by both the objective properties of the images and the subjective aspects arising from the viewer’s internal process. Traditional AI research on aesthetics assessment has largely focused on objective properties or group level evaluations, often overlooking individual subjectivity. Large Language Models (LLMs), trained on vast text data, offer potential for addressing this gap through their semantic and contextual understanding capabilities. We hypothesized that their capabilities could be leveraged for predicting aesthetic evaluations and explored effective prompting techniques using a proposed list of evaluation factors. We found that the factors related to semantic and contextual understanding enhance LLMs’ performance in predicting beauty. This study shows the potential of a novel approach to image aesthetics assessment by leveraging the semantic and contextual understanding capabilities of LLMs.