用大模型自动分析艺术作品，揭示风格演变规律
CognArtive: Large Language Models for Automating Art Analysis and Decoding Aesthetic Elements
- 用大模型解析画作的视觉元素、构图与技法
- 可快速分析海量画作，发现跨时代的艺术趋势
- 适合艺术史研究者与数字人文学者使用
艺术作为普遍语言，其内涵丰富且多义。随着大语言模型（LLMs）和多模态大语言模型（MLLMs）的发展，人们开始思考这些模型能否用于评估与解读艺术品中的艺术元素。尽管已有相关研究，但据我们所知，尚未深入探索利用大模型对艺术品的技术特征与表达内涵进行细致分析。本研究致力于自动化形式化艺术分析框架，实现对大量艺术作品的高效分析，并考察其风格模式随时间的演变。我们探索了大模型如何解码艺术表达、视觉元素、构图与技法，揭示出跨越历史时期的新兴模式。最后，讨论了大模型在此任务中的优劣，强调其处理海量艺术数据并生成深刻见解的能力。由于结果详尽且颗粒度精细，我们开发了交互式数据可视化工具，可在 https://cognartive.github.io/ 在线访问，以提升理解与可及性。
原文摘要 · Abstract (English)
Art, as a universal language, can be interpreted in diverse ways, with artworks embodying profound meanings and nuances. The advent of Large Language Models (LLMs) and the availability of Multimodal Large Language Models (MLLMs) raise the question of how these transformative models can be used to assess and interpret the artistic elements of artworks. While research has been conducted in this domain, to the best of our knowledge, a deep and detailed understanding of the technical and expressive features of artworks using LLMs has not been explored. In this study, we investigate the automation of a formal art analysis framework to analyze a high-throughput number of artworks rapidly and examine how their patterns evolve over time. We explore how LLMs can decode artistic expressions, visual elements, composition, and techniques, revealing emerging patterns that develop across periods. Finally, we discuss the strengths and limitations of LLMs in this context, emphasizing their ability to process vast quantities of art-related data and generate insightful interpretations. Due to the exhaustive and granular nature of the results, we have developed interactive data visualizations, available online https://cognartive.github.io/, to enhance understanding and accessibility.
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