@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 The 6th Workshop on Narrative Understanding",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T Using Large Language Models for Understanding Narrative Discourse
%A Piper, Andrew
%A Bagga, Sunyam
%Y Lal, Yash Kumar
%Y Clark, Elizabeth
%Y Iyyer, Mohit
%Y Chaturvedi, Snigdha
%Y Brei, Anneliese
%Y Brahman, Faeze
%Y Chandu, Khyathi Raghavi
%S Proceedings of the The 6th Workshop on Narrative Understanding
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F piper-bagga-2024-using
%X 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.
%U https://aclanthology.org/2024.wnu-1.4
%P 37-46
Markdown (Informal)
[Using Large Language Models for Understanding Narrative Discourse](https://aclanthology.org/2024.wnu-1.4) (Piper & Bagga, WNU 2024)
ACL