@inproceedings{zhang-etal-2026-epipersona,
title = "{E}pi{P}ersona: Persona Projection and Episode Coupling for Pluralistic Preference Modeling",
author = "Zhang, Yujie and
Yuan, Weikang and
Jiang, Zhuoren and
Yan, Pengwei",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2413/",
pages = "54804--54823",
ISBN = "979-8-89176-459-0",
abstract = "Pluralistic alignment is essential for adapting large language models (LLMs) to the diverse preferences of individuals and minority groups. However, existing personalized reward models often mix stable personal traits with episode-specific factors, limiting their ability to generalize across episodes. To address this challenge, we propose EpiPersona, a framework for explicit persona{--}episode coupling. EpiPersona first projects noisy preference feedback into a low-dimensional persona space, where similar personas are aggregated into shared discrete codewords. This process separates enduring personal characteristics from situational signals without relying on predefined preference dimensions. The inferred persona representation is then coupled with the current episode, enabling episode-aware preference prediction. Extensive experiments show that EpiPersona outperforms the baselines. It achieves notable performance gains in hard episodic-shift scenarios, while remaining effective with sparse preference data."
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<abstract>Pluralistic alignment is essential for adapting large language models (LLMs) to the diverse preferences of individuals and minority groups. However, existing personalized reward models often mix stable personal traits with episode-specific factors, limiting their ability to generalize across episodes. To address this challenge, we propose EpiPersona, a framework for explicit persona–episode coupling. EpiPersona first projects noisy preference feedback into a low-dimensional persona space, where similar personas are aggregated into shared discrete codewords. This process separates enduring personal characteristics from situational signals without relying on predefined preference dimensions. The inferred persona representation is then coupled with the current episode, enabling episode-aware preference prediction. Extensive experiments show that EpiPersona outperforms the baselines. It achieves notable performance gains in hard episodic-shift scenarios, while remaining effective with sparse preference data.</abstract>
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%0 Conference Proceedings
%T EpiPersona: Persona Projection and Episode Coupling for Pluralistic Preference Modeling
%A Zhang, Yujie
%A Yuan, Weikang
%A Jiang, Zhuoren
%A Yan, Pengwei
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhang-etal-2026-epipersona
%X Pluralistic alignment is essential for adapting large language models (LLMs) to the diverse preferences of individuals and minority groups. However, existing personalized reward models often mix stable personal traits with episode-specific factors, limiting their ability to generalize across episodes. To address this challenge, we propose EpiPersona, a framework for explicit persona–episode coupling. EpiPersona first projects noisy preference feedback into a low-dimensional persona space, where similar personas are aggregated into shared discrete codewords. This process separates enduring personal characteristics from situational signals without relying on predefined preference dimensions. The inferred persona representation is then coupled with the current episode, enabling episode-aware preference prediction. Extensive experiments show that EpiPersona outperforms the baselines. It achieves notable performance gains in hard episodic-shift scenarios, while remaining effective with sparse preference data.
%U https://aclanthology.org/2026.emnlp-main.2413/
%P 54804-54823
Markdown (Informal)
[EpiPersona: Persona Projection and Episode Coupling for Pluralistic Preference Modeling](https://aclanthology.org/2026.emnlp-main.2413/) (Zhang et al., EMNLP 2026)
ACL