@inproceedings{zhang-etal-2025-roleplot,
title = "{R}ole{P}lot: A Systematic Framework for Evaluating and Enhancing the Plot-Progression Capabilities of Role-Playing Agents",
author = "Zhang, Pinyi and
An, Siyu and
Qiao, Lingfeng and
Yu, Yifei and
Chen, Jingyang and
Wang, Jie and
Yin, Di and
Sun, Xing and
Zhang, Kai",
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.603/",
doi = "10.18653/v1/2025.acl-long.603",
pages = "12337--12354",
ISBN = "979-8-89176-251-0",
abstract = "Role-playing agents (RPAs) are garnering increasing interests as a novel form of conversational AI. While previous research has predominantly concentrated on their ability to portray specified characters, we argue from a user-centered perspective that RPAs' capability to advance the plot requires substantial improvements to deliver more engaging interaction. To bridge this gap, we propose RolePlot, a role-playing framework specifically designed to evaluate and enhance the plot-progression capabilities of RPAs. RolePlot begins by constructing a plot-progression dataset extended from human-written literary scripts and specially designed synthetic data, followed by narrative theory-driven manual annotation and automated labeling validated through human verification. We then exploit the over-parameterized embedding space of LLMs to detect a ``trigger subspace'' that identifies dialogue segments catalyzing plot transitions. When user{'}s inputs align with this subspace, we explicitly prompt RPAs to advance the plot. For evaluation, we simulate User-RPA interactions and track both the conversation longevity (measured in dialogue turns before disengagement) and users' arousal levels across different stages. Empirically, our method improves RPAs' capability to time plot developments, and more importantly, yielding a significant increase in conversation turns and sustained higher arousal levels, thereby confirming that users experience more immersive engagements."
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<abstract>Role-playing agents (RPAs) are garnering increasing interests as a novel form of conversational AI. While previous research has predominantly concentrated on their ability to portray specified characters, we argue from a user-centered perspective that RPAs’ capability to advance the plot requires substantial improvements to deliver more engaging interaction. To bridge this gap, we propose RolePlot, a role-playing framework specifically designed to evaluate and enhance the plot-progression capabilities of RPAs. RolePlot begins by constructing a plot-progression dataset extended from human-written literary scripts and specially designed synthetic data, followed by narrative theory-driven manual annotation and automated labeling validated through human verification. We then exploit the over-parameterized embedding space of LLMs to detect a “trigger subspace” that identifies dialogue segments catalyzing plot transitions. When user’s inputs align with this subspace, we explicitly prompt RPAs to advance the plot. For evaluation, we simulate User-RPA interactions and track both the conversation longevity (measured in dialogue turns before disengagement) and users’ arousal levels across different stages. Empirically, our method improves RPAs’ capability to time plot developments, and more importantly, yielding a significant increase in conversation turns and sustained higher arousal levels, thereby confirming that users experience more immersive engagements.</abstract>
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%0 Conference Proceedings
%T RolePlot: A Systematic Framework for Evaluating and Enhancing the Plot-Progression Capabilities of Role-Playing Agents
%A Zhang, Pinyi
%A An, Siyu
%A Qiao, Lingfeng
%A Yu, Yifei
%A Chen, Jingyang
%A Wang, Jie
%A Yin, Di
%A Sun, Xing
%A Zhang, Kai
%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 zhang-etal-2025-roleplot
%X Role-playing agents (RPAs) are garnering increasing interests as a novel form of conversational AI. While previous research has predominantly concentrated on their ability to portray specified characters, we argue from a user-centered perspective that RPAs’ capability to advance the plot requires substantial improvements to deliver more engaging interaction. To bridge this gap, we propose RolePlot, a role-playing framework specifically designed to evaluate and enhance the plot-progression capabilities of RPAs. RolePlot begins by constructing a plot-progression dataset extended from human-written literary scripts and specially designed synthetic data, followed by narrative theory-driven manual annotation and automated labeling validated through human verification. We then exploit the over-parameterized embedding space of LLMs to detect a “trigger subspace” that identifies dialogue segments catalyzing plot transitions. When user’s inputs align with this subspace, we explicitly prompt RPAs to advance the plot. For evaluation, we simulate User-RPA interactions and track both the conversation longevity (measured in dialogue turns before disengagement) and users’ arousal levels across different stages. Empirically, our method improves RPAs’ capability to time plot developments, and more importantly, yielding a significant increase in conversation turns and sustained higher arousal levels, thereby confirming that users experience more immersive engagements.
%R 10.18653/v1/2025.acl-long.603
%U https://aclanthology.org/2025.acl-long.603/
%U https://doi.org/10.18653/v1/2025.acl-long.603
%P 12337-12354
Markdown (Informal)
[RolePlot: A Systematic Framework for Evaluating and Enhancing the Plot-Progression Capabilities of Role-Playing Agents](https://aclanthology.org/2025.acl-long.603/) (Zhang et al., ACL 2025)
ACL
- Pinyi Zhang, Siyu An, Lingfeng Qiao, Yifei Yu, Jingyang Chen, Jie Wang, Di Yin, Xing Sun, and Kai Zhang. 2025. RolePlot: A Systematic Framework for Evaluating and Enhancing the Plot-Progression Capabilities of Role-Playing Agents. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12337–12354, Vienna, Austria. Association for Computational Linguistics.