@inproceedings{piper-bagga-2024-using,
    title = "Using Large Language Models for Understanding Narrative Discourse",
    author = "Piper, Andrew  and
      Bagga, Sunyam",
    editor = "Lal, Yash Kumar  and
      Clark, Elizabeth  and
      Iyyer, Mohit  and
      Chaturvedi, Snigdha  and
      Brei, Anneliese  and
      Brahman, Faeze  and
      Chandu, Khyathi Raghavi",
    booktitle = "Proceedings of the 6th Workshop on Narrative Understanding",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.wnu-1.4/",
    doi = "10.18653/v1/2024.wnu-1.4",
    pages = "37--46",
    abstract = "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."
}