Structuralist Approach to AI Literary Criticism: Leveraging Greimas Semiotic Square for Large Language Models

TL;DR

This paper introduces GLASS, a semiotic square-based framework that enhances large language models' ability to perform in-depth, structured literary criticism, bridging AI and literary analysis.

Contribution

It presents the first GSS-based dataset and quantitative metrics for AI-driven literary criticism, improving LLMs' analytical depth and quality.

Findings

High performance of GLASS compared to expert criticism

Successful analysis of 39 classic literary works

Introduction of a new dataset for GSS-based literary analysis

Abstract

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.