@inproceedings{ye-etal-2025-generic,
title = "From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment",
author = "Ye, Jing and
Xiang, Lu and
Zhang, Yaping and
Zong, Chengqing",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1024/",
pages = "18826--18853",
ISBN = "979-8-89176-335-7",
abstract = "Effective emotional support hinges on understanding users' emotions and needs to provide meaningful comfort during multi-turn interactions. Large Language Models (LLMs) show great potential for expressing empathy; however, they often deliver generic responses that fail to address users' specific needs. To tackle this issue, we propose a self-evolution framework designed to help LLMs improve their responses to better align with users' implicit preferences concerning personality, emotional state, and specific context. Our framework consists of two distinct phases: \textit{(1)} \textit{Emotional Support Experience Acquisition}, where LLMs are fine-tuned on limited emotional support conversation data to provide basic support, and \textit{(2)} \textit{Self-Improvement for Personalized Emotional Support}, where LLMs leverage self-reflection and self-refinement to generate personalized responses. Through iterative direct preference optimization between the pre- and post-refined responses, our model generates responses that reflect a better understanding of the user{'}s implicit preferences. Extensive experiments and evaluations demonstrate that our method significantly enhances the model{'}s performance in emotional support, reducing unhelpful responses and minimizing discrepancies between user preferences and model outputs."
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<abstract>Effective emotional support hinges on understanding users’ emotions and needs to provide meaningful comfort during multi-turn interactions. Large Language Models (LLMs) show great potential for expressing empathy; however, they often deliver generic responses that fail to address users’ specific needs. To tackle this issue, we propose a self-evolution framework designed to help LLMs improve their responses to better align with users’ implicit preferences concerning personality, emotional state, and specific context. Our framework consists of two distinct phases: (1) Emotional Support Experience Acquisition, where LLMs are fine-tuned on limited emotional support conversation data to provide basic support, and (2) Self-Improvement for Personalized Emotional Support, where LLMs leverage self-reflection and self-refinement to generate personalized responses. Through iterative direct preference optimization between the pre- and post-refined responses, our model generates responses that reflect a better understanding of the user’s implicit preferences. Extensive experiments and evaluations demonstrate that our method significantly enhances the model’s performance in emotional support, reducing unhelpful responses and minimizing discrepancies between user preferences and model outputs.</abstract>
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%0 Conference Proceedings
%T From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment
%A Ye, Jing
%A Xiang, Lu
%A Zhang, Yaping
%A Zong, Chengqing
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F ye-etal-2025-generic
%X Effective emotional support hinges on understanding users’ emotions and needs to provide meaningful comfort during multi-turn interactions. Large Language Models (LLMs) show great potential for expressing empathy; however, they often deliver generic responses that fail to address users’ specific needs. To tackle this issue, we propose a self-evolution framework designed to help LLMs improve their responses to better align with users’ implicit preferences concerning personality, emotional state, and specific context. Our framework consists of two distinct phases: (1) Emotional Support Experience Acquisition, where LLMs are fine-tuned on limited emotional support conversation data to provide basic support, and (2) Self-Improvement for Personalized Emotional Support, where LLMs leverage self-reflection and self-refinement to generate personalized responses. Through iterative direct preference optimization between the pre- and post-refined responses, our model generates responses that reflect a better understanding of the user’s implicit preferences. Extensive experiments and evaluations demonstrate that our method significantly enhances the model’s performance in emotional support, reducing unhelpful responses and minimizing discrepancies between user preferences and model outputs.
%U https://aclanthology.org/2025.findings-emnlp.1024/
%P 18826-18853
Markdown (Informal)
[From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment](https://aclanthology.org/2025.findings-emnlp.1024/) (Ye et al., Findings 2025)
ACL