Analyzing Nobel Prize Literature with Large Language Models

TL;DR

This paper evaluates the ability of large language models to analyze Nobel Prize-winning literature, comparing AI and human interpretations to assess strengths and limitations in understanding complex literary elements.

Contribution

It introduces a novel comparison of LLMs and humans in literary analysis of Nobel Prize stories, highlighting AI's analytical strengths and emotional limitations.

Findings

LLMs excel in structured thematic analysis

Humans outperform in emotional nuance and coherence

AI shows potential for collaboration in humanities

Abstract

This study examines the capabilities of advanced Large Language Models (LLMs), particularly the o1 model, in the context of literary analysis. The outputs of these models are compared directly to those produced by graduate-level human participants. By focusing on two Nobel Prize-winning short stories, 'Nine Chapters' by Han Kang, the 2024 laureate, and 'Friendship' by Jon Fosse, the 2023 laureate, the research explores the extent to which AI can engage with complex literary elements such as thematic analysis, intertextuality, cultural and historical contexts, linguistic and structural innovations, and character development. Given the Nobel Prize's prestige and its emphasis on cultural, historical, and linguistic richness, applying LLMs to these works provides a deeper understanding of both human and AI approaches to interpretation. The study uses qualitative and quantitative evaluations of coherence, creativity, and fidelity to the text, revealing the strengths and limitations of AI in tasks typically reserved for human expertise. While LLMs demonstrate strong analytical capabilities, particularly in structured tasks, they often fall short in emotional nuance and coherence, areas where human interpretation excels. This research underscores the potential for human-AI collaboration in the humanities, opening new opportunities in literary studies and beyond.