Tracking Evolving Relationship Between Characters in Books in the Era of Large Language Models

Abhilasha Sancheti; Rachel Rudinger

doi:10.18653/v1/2025.wnu-1.12

Tracking Evolving Relationship Between Characters in Books in the Era of Large Language Models

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Abstract

This work aims to assess the zero-shot social reasoning capabilities of LLMs by proposing various strategies based on the granularity of information used to track the fine-grained evolution in the relationship between characters in a book. Without gold annotations, we thoroughly analyze the agreements between predictions from multiple LLMs and manually examine their consensus at a local and global level via the task of trope prediction. Our findings reveal low-to-moderate agreement among LLMs and humans, reflecting the complexity of the task. Analysis shows that LLMs are sensitive to subtle contextual changes and often rely on surface-level cues. Humans, too, may interpret relationships differently, leading to disagreements in annotations.

Anthology ID:: 2025.wnu-1.12
Volume:: Proceedings of the The 7th Workshop on Narrative Understanding
Month:: May
Year:: 2025
Address:: Albuquerque, New Mexico
Editors:: Elizabeth Clark, Yash Kumar Lal, Snigdha Chaturvedi, Mohit Iyyer, Anneliese Brei, Ashutosh Modi, Khyathi Raghavi Chandu
Venues:: WNU | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 64–82
Language:
URL:: https://aclanthology.org/2025.wnu-1.12/
DOI:: 10.18653/v1/2025.wnu-1.12
Bibkey:
Cite (ACL):: Abhilasha Sancheti and Rachel Rudinger. 2025. Tracking Evolving Relationship Between Characters in Books in the Era of Large Language Models. In Proceedings of the The 7th Workshop on Narrative Understanding, pages 64–82, Albuquerque, New Mexico. Association for Computational Linguistics.
Cite (Informal):: Tracking Evolving Relationship Between Characters in Books in the Era of Large Language Models (Sancheti & Rudinger, WNU 2025)
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PDF:: https://aclanthology.org/2025.wnu-1.12.pdf

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@inproceedings{sancheti-rudinger-2025-tracking,
    title = "Tracking Evolving Relationship Between Characters in Books in the Era of Large Language Models",
    author = "Sancheti, Abhilasha  and
      Rudinger, Rachel",
    editor = "Clark, Elizabeth  and
      Lal, Yash Kumar  and
      Chaturvedi, Snigdha  and
      Iyyer, Mohit  and
      Brei, Anneliese  and
      Modi, Ashutosh  and
      Chandu, Khyathi Raghavi",
    booktitle = "Proceedings of the The 7th Workshop on Narrative Understanding",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.wnu-1.12/",
    doi = "10.18653/v1/2025.wnu-1.12",
    pages = "64--82",
    ISBN = "979-8-89176-247-3",
    abstract = "This work aims to assess the zero-shot social reasoning capabilities of LLMs by proposing various strategies based on the granularity of information used to track the fine-grained evolution in the relationship between characters in a book. Without gold annotations, we thoroughly analyze the agreements between predictions from multiple LLMs and manually examine their consensus at a local and global level via the task of trope prediction. Our findings reveal low-to-moderate agreement among LLMs and humans, reflecting the complexity of the task. Analysis shows that LLMs are sensitive to subtle contextual changes and often rely on surface-level cues. Humans, too, may interpret relationships differently, leading to disagreements in annotations."
}

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%0 Conference Proceedings
%T Tracking Evolving Relationship Between Characters in Books in the Era of Large Language Models
%A Sancheti, Abhilasha
%A Rudinger, Rachel
%Y Clark, Elizabeth
%Y Lal, Yash Kumar
%Y Chaturvedi, Snigdha
%Y Iyyer, Mohit
%Y Brei, Anneliese
%Y Modi, Ashutosh
%Y Chandu, Khyathi Raghavi
%S Proceedings of the The 7th Workshop on Narrative Understanding
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-247-3
%F sancheti-rudinger-2025-tracking
%X This work aims to assess the zero-shot social reasoning capabilities of LLMs by proposing various strategies based on the granularity of information used to track the fine-grained evolution in the relationship between characters in a book. Without gold annotations, we thoroughly analyze the agreements between predictions from multiple LLMs and manually examine their consensus at a local and global level via the task of trope prediction. Our findings reveal low-to-moderate agreement among LLMs and humans, reflecting the complexity of the task. Analysis shows that LLMs are sensitive to subtle contextual changes and often rely on surface-level cues. Humans, too, may interpret relationships differently, leading to disagreements in annotations.
%R 10.18653/v1/2025.wnu-1.12
%U https://aclanthology.org/2025.wnu-1.12/
%U https://doi.org/10.18653/v1/2025.wnu-1.12
%P 64-82

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Markdown (Informal)

[Tracking Evolving Relationship Between Characters in Books in the Era of Large Language Models](https://aclanthology.org/2025.wnu-1.12/) (Sancheti & Rudinger, WNU 2025)

Tracking Evolving Relationship Between Characters in Books in the Era of Large Language Models (Sancheti & Rudinger, WNU 2025)

ACL

Abhilasha Sancheti and Rachel Rudinger. 2025. Tracking Evolving Relationship Between Characters in Books in the Era of Large Language Models. In Proceedings of the The 7th Workshop on Narrative Understanding, pages 64–82, Albuquerque, New Mexico. Association for Computational Linguistics.