用符号学框架让AI读懂复杂文学作品的深层意义
Structuralist Approach to AI Literary Criticism: Leveraging Greimas Semiotic Square for Large Language Models
- 基于格雷马斯符号学方阵构建分析框架，引导AI拆解叙事结构
- 创建首个基于符号学的48部作品批评数据集，量化评估效果
- 可生成高质量文学分析，适合研究者与教育场景使用
大型语言模型在文本理解与生成方面表现优异，但在处理思想深刻、结构复杂的文学作品时难以提供专业的文学批评。本文提出GLASS（Greimas Literary Analysis via Semiotic Square），一种基于格雷马斯符号学方阵（GSS）的结构化分析框架，以提升LLM进行深度文学分析的能力。GLASS能快速解析叙事结构与深层含义。我们构建了首个基于GSS的文学批评数据集，包含48部作品的详尽分析；并提出基于LLM作为评判者的量化评估指标。实验显示，该框架在多部作品和多个LLM上的表现均接近专家水平。最后，我们将GLASS应用于39部经典作品，生成了原创且高质量的分析，填补了现有研究空白。本研究为文学研究与教育提供了基于AI的分析工具，揭示了文学感知的认知机制。
原文摘要 · Abstract (English)
Large Language Models (LLMs) excel in understanding and generating text but struggle with providing professional literary criticism for works with profound thoughts and complex narratives. This paper proposes GLASS (Greimas Literary Analysis via Semiotic Square), a structured analytical framework based on Greimas Semiotic Square (GSS), to enhance LLMs' ability to conduct in-depth literary analysis. GLASS facilitates the rapid dissection of narrative structures and deep meanings in narrative works. We propose the first dataset for GSS-based literary criticism, featuring detailed analyses of 48 works. Then we propose quantitative metrics for GSS-based literary criticism using the LLM-as-a-judge paradigm. Our framework's results, compared with expert criticism across multiple works and LLMs, show high performance. Finally, we applied GLASS to 39 classic works, producing original and high-quality analyses that address existing research gaps. This research provides an AI-based tool for literary research and education, offering insights into the cognitive mechanisms underlying literary engagement.
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