@inproceedings{zheng-etal-2026-empathy,
title = "From Empathy to Personalized Empathy: Adapting Empathetic Strategies to Individual Users",
author = "Zheng, Wuqiang and
Wang, Chengbing and
Yang, Yilin and
Cheng, Junyi and
Xiao, Jianfei and
HU, Sun and
Xie, Yi and
Li, Yangyang and
Wang, Wenjie",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2227/",
pages = "47692--47709",
ISBN = "979-8-89176-462-0",
abstract = "As Large Language Models (LLMs) are increasingly deployed in long-term interactions with users, empathy has become an increasingly important capability. However, existing research overlooks the influence of users' personality traits on empathetic strategies during long-term interactions. To address this gap, we introduce the task of personalized empathy, which focuses on adapting empathetic strategies according to users' personalized characteristics derived from history. To study and enhance this capability, we construct PersonaEmp, a personalized empathy dataset built from long-term user-AI interactions, featuring rich user histories, persona information, and empathy-seeking queries. We further propose PereGRM, a reward modeling framework that combines the empathy evaluation structure with dynamic evaluation criteria generation for fine-grained reward modeling. Experimental results across different settings and multiple judge models show that PereGRM consistently achieves the strongest performance improvements, indicating its effectiveness for enhancing personalized empathetic capabilities."
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<abstract>As Large Language Models (LLMs) are increasingly deployed in long-term interactions with users, empathy has become an increasingly important capability. However, existing research overlooks the influence of users’ personality traits on empathetic strategies during long-term interactions. To address this gap, we introduce the task of personalized empathy, which focuses on adapting empathetic strategies according to users’ personalized characteristics derived from history. To study and enhance this capability, we construct PersonaEmp, a personalized empathy dataset built from long-term user-AI interactions, featuring rich user histories, persona information, and empathy-seeking queries. We further propose PereGRM, a reward modeling framework that combines the empathy evaluation structure with dynamic evaluation criteria generation for fine-grained reward modeling. Experimental results across different settings and multiple judge models show that PereGRM consistently achieves the strongest performance improvements, indicating its effectiveness for enhancing personalized empathetic capabilities.</abstract>
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%0 Conference Proceedings
%T From Empathy to Personalized Empathy: Adapting Empathetic Strategies to Individual Users
%A Zheng, Wuqiang
%A Wang, Chengbing
%A Yang, Yilin
%A Cheng, Junyi
%A Xiao, Jianfei
%A HU, Sun
%A Xie, Yi
%A Li, Yangyang
%A Wang, Wenjie
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zheng-etal-2026-empathy
%X As Large Language Models (LLMs) are increasingly deployed in long-term interactions with users, empathy has become an increasingly important capability. However, existing research overlooks the influence of users’ personality traits on empathetic strategies during long-term interactions. To address this gap, we introduce the task of personalized empathy, which focuses on adapting empathetic strategies according to users’ personalized characteristics derived from history. To study and enhance this capability, we construct PersonaEmp, a personalized empathy dataset built from long-term user-AI interactions, featuring rich user histories, persona information, and empathy-seeking queries. We further propose PereGRM, a reward modeling framework that combines the empathy evaluation structure with dynamic evaluation criteria generation for fine-grained reward modeling. Experimental results across different settings and multiple judge models show that PereGRM consistently achieves the strongest performance improvements, indicating its effectiveness for enhancing personalized empathetic capabilities.
%U https://aclanthology.org/2026.findings-emnlp.2227/
%P 47692-47709
Markdown (Informal)
[From Empathy to Personalized Empathy: Adapting Empathetic Strategies to Individual Users](https://aclanthology.org/2026.findings-emnlp.2227/) (Zheng et al., Findings 2026)
ACL
- Wuqiang Zheng, Chengbing Wang, Yilin Yang, Junyi Cheng, Jianfei Xiao, Sun HU, Yi Xie, Yangyang Li, and Wenjie Wang. 2026. From Empathy to Personalized Empathy: Adapting Empathetic Strategies to Individual Users. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 47692–47709, Budapest, Hungary. Association for Computational Linguistics.