Using Large Language Models for Understanding Narrative Discourse

In this study, we explore the application of large language models (LLMs) to analyze narrative discourse within the framework established by the field of narratology.We develop a set of elementary narrative features derived from prior theoretical work that focus on core dimensions of narrative, including time, setting, and perspective.Through experiments with GPT-4 and fine-tuned open-source models like Llama3, we demonstrate the models' ability to annotate narrative passages with reasonable levels of agreement with human annotators.Leveraging a dataset of human-annotated passages spanning 18 distinct narrative and non-narrative genres, our work provides empirical support for the deictic theory of narrative communication.This theory posits that a fundamental function of storytelling is the focalization of attention on distant human experiences to facilitate social coordination.We conclude with a discussion of the possibilities for LLM-driven narrative discourse understanding.