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This paper explores the intersection of literary analysis and AI, arguing that large language models (LLMs) require a more nuanced understanding of cultural contexts to avoid monolingual biases. By applying frameworks from literary disciplines, the authors propose a layered approach that enhances AI's cultural literacy through comparative reading and critical theory. The key finding suggests that integrating world literature methodologies can significantly improve AI's ability to navigate complex textual landscapes and global narratives.
Rethinking LLMs through the lens of world literature could revolutionize how AI interprets and engages with diverse cultural narratives.
LLMs stage a new form of cultural encounter that is massive, automated, and monolingual. Literary disciplines have always negotiated cultural struggles with comparative reading of literature, narratological and poetic analysis, critical theory, world literature, and translation. These tools have now become indispensable for building culturally literate AI. The essay develops a layered framework toward more nuanced textual models and pluralistic interpretations of AI, emphasizing the natural intersections of literature and AI development, connecting current debates in critical theory with structural monolingualism, and suggesting a new application of world literature approaches to address global AI textuality through macrostructure, circulation, and untranslatability.
BAIR