Zero-Shot Classification of Art With Large Language Models

Art has become an important new investment vehicle. Thus, interest is growing in art price prediction as a tool for assessing the returns and risks of art investments. Both traditional statistical methods and machine learning methods have been used to predict art prices. However, both methods incur substantial human costs for data preprocessing for the construction of prediction models, necessitating a reduction in the workload. In this study, we propose the zero-shot classification method to perform automatic annotation in data processing for art price prediction by leveraging large language models (LLMs). The proposed method can perform annotation without new training data. Thus, it minimizes human costs. Our experiments demonstrated that the 4-bit quantized Llama-3 70B model, which can run on a local server, achieved the most accurate (over 0.9) automatic annotation of different art forms using LLMs, performing slightly better than the GPT-4o model from OpenAI. These results are practical for data preprocessing and comparable with the results of previous machine learning methods.