Accurately predicting individual aesthetic evaluation for images is a fundamental challenge for AI. Various deep learning (DL)-based models have been proposed for this task, training on image evaluation data to extract objective low-level features. However, aesthetic preferences are inherently subjective and individual-dependent. Accurate prediction thus requires the extraction of high-level semantic features of images and the active collection of preference information from the target individual. To address this issue, we focus on the utility of Large Language Models (LLMs) pretrained on vast amounts of textual data, and develop an integrated DL-LLM system. The system actively elicits aesthetic preferences through LLM-based semi-structured interviews and predicts aesthetic evaluation by leveraging both low-level and high-level features. In our experiments, we compare the proposed system against conventional systems, human predictors, and the target individual's own re-evaluations after a certain time interval. Our results show that the proposed system outperforms all of them, with particularly strong performance on highly-rated images. Moreover, the prediction error of the proposed system is smaller than within-person variability, while human predictors show the largest error, likely due to the influence of their own aesthetic values. These results suggest that AI may be better positioned than others or one's future self to capture individual aesthetic preferences at a given point. This opens a new question of whether AI could serve as a deeper interpreter of human aesthetic sensibility than humans themselves.

翻译：精准预测个体对图像的美学评价是人工智能面临的基础挑战。为此，已提出多种基于深度学习的模型，通过训练图像评估数据提取客观低层特征。然而，审美偏好本质上是主观且因人而异的，准确预测需要提取图像的高层语义特征，并主动收集目标个体的偏好信息。针对这一问题，我们聚焦于在大规模文本数据上预训练的大语言模型的应用，开发了一种集成的深度学习-大语言模型系统。该系统通过基于大语言模型的半结构化访谈主动获取审美偏好，并融合低层与高层特征预测美学评价。实验中，我们将所提系统与传统系统、人类预测者及目标个体经过一段时间间隔后的自我重评进行比较。结果表明，所提系统在所有对比中表现最优，尤其在评价较高的图像上优势显著。此外，所提系统的预测误差小于个体自身变异，而人类预测者误差最大，这可能源于其自身审美价值观的影响。这些结果表明，AI可能比他人或个体未来的自我更能准确捕捉特定时间点的审美偏好。这提出了一个新问题：AI是否能够比人类自身更深入地解读人类审美感受力。