@inproceedings{brei-etal-2025-classifying,
title = "Classifying Unreliable Narrators with Large Language Models",
author = "Brei, Anneliese and
Henry, Katharine and
Sharma, Abhisheik and
Srivastava, Shashank and
Chaturvedi, Snigdha",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1013/",
doi = "10.18653/v1/2025.acl-long.1013",
pages = "20766--20791",
ISBN = "979-8-89176-251-0",
abstract = "Often when we interact with a first-person account of events, we consider whether or not the narrator, the primary speaker of the text, is reliable. In this paper, we propose using computational methods to identify unreliable narrators, i.e. those who unintentionally misrepresent information. Borrowing literary theory from narratology to define different types of unreliable narrators based on a variety of textual phenomena, we present TUNa, a human-annotated dataset of narratives from multiple domains, including blog posts, subreddit posts, hotel reviews, and works of literature. We define classification tasks for intra-narrational, inter-narrational, and inter-textual unreliabilities and analyze the performance of popular open-weight and proprietary LLMs for each. We propose learning from literature to perform unreliable narrator classification on real-world text data. To this end, we experiment with few-shot, fine-tuning, and curriculum learning settings. Our results show that this task is very challenging, and there is potential for using LLMs to identify unreliable narrators. We release our expert-annotated dataset and code at https://github.com/adbrei/unreliable-narrators and invite future research in this area."
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<abstract>Often when we interact with a first-person account of events, we consider whether or not the narrator, the primary speaker of the text, is reliable. In this paper, we propose using computational methods to identify unreliable narrators, i.e. those who unintentionally misrepresent information. Borrowing literary theory from narratology to define different types of unreliable narrators based on a variety of textual phenomena, we present TUNa, a human-annotated dataset of narratives from multiple domains, including blog posts, subreddit posts, hotel reviews, and works of literature. We define classification tasks for intra-narrational, inter-narrational, and inter-textual unreliabilities and analyze the performance of popular open-weight and proprietary LLMs for each. We propose learning from literature to perform unreliable narrator classification on real-world text data. To this end, we experiment with few-shot, fine-tuning, and curriculum learning settings. Our results show that this task is very challenging, and there is potential for using LLMs to identify unreliable narrators. We release our expert-annotated dataset and code at https://github.com/adbrei/unreliable-narrators and invite future research in this area.</abstract>
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%0 Conference Proceedings
%T Classifying Unreliable Narrators with Large Language Models
%A Brei, Anneliese
%A Henry, Katharine
%A Sharma, Abhisheik
%A Srivastava, Shashank
%A Chaturvedi, Snigdha
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F brei-etal-2025-classifying
%X Often when we interact with a first-person account of events, we consider whether or not the narrator, the primary speaker of the text, is reliable. In this paper, we propose using computational methods to identify unreliable narrators, i.e. those who unintentionally misrepresent information. Borrowing literary theory from narratology to define different types of unreliable narrators based on a variety of textual phenomena, we present TUNa, a human-annotated dataset of narratives from multiple domains, including blog posts, subreddit posts, hotel reviews, and works of literature. We define classification tasks for intra-narrational, inter-narrational, and inter-textual unreliabilities and analyze the performance of popular open-weight and proprietary LLMs for each. We propose learning from literature to perform unreliable narrator classification on real-world text data. To this end, we experiment with few-shot, fine-tuning, and curriculum learning settings. Our results show that this task is very challenging, and there is potential for using LLMs to identify unreliable narrators. We release our expert-annotated dataset and code at https://github.com/adbrei/unreliable-narrators and invite future research in this area.
%R 10.18653/v1/2025.acl-long.1013
%U https://aclanthology.org/2025.acl-long.1013/
%U https://doi.org/10.18653/v1/2025.acl-long.1013
%P 20766-20791
Markdown (Informal)
[Classifying Unreliable Narrators with Large Language Models](https://aclanthology.org/2025.acl-long.1013/) (Brei et al., ACL 2025)
ACL
- Anneliese Brei, Katharine Henry, Abhisheik Sharma, Shashank Srivastava, and Snigdha Chaturvedi. 2025. Classifying Unreliable Narrators with Large Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 20766–20791, Vienna, Austria. Association for Computational Linguistics.