From Generic Empathy to Personalized Emotional Support: A Self-Evolution Framework for User Preference Alignment (2025)

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.

## introduction emotional support conversation (esc) systems require a deep understanding of users' emotions @xcite and need to provide meaningful comfort and assistance during multi-turn interactions @xcite , which are vital in practical applications such as mental health care, emotional companionship, and customer service. given that each user has unique i've been feeling really down since my best friend passed away in an accident. losing a close friend suddenly in an accident is really hard to accept. 3 years ago, my childhood friend also passed away in a car accident on a rainy day. i often leave messages on his old blog posts. maybe you could do something similar-share your daily life and thoughts to him, as if he just went abroad to study. i understand how difficult it must be for you. losing a best friend who was like a sibling is an immense loss. take your time to process your emotions. thank you for understanding. it's just hard to accept that he is no longer with me. the everyday moments we shared, like having meals together and going to school, are still vivid in my mind. ## method inspired by recent insights highlighting that llms can self-improve through language feedback @xcite @xcite , we present a self-evolution framework designed to enable llms to provide personalized emotional support. this framework operates in two phases: emotional support experience acquisition (section 2.1) and self-improvement for personalized emotional support (section 2.2). ## analysis and discussion this section aims to address the following key questions: q1: does the model exhibit self-reflection and selfrefinement capabilities to learn the user's implicit preferences from the ongoing dialogue? q2: does self-refinement lead to better emotional support responses? q3: what's the advantage of the synthetic preference data in our framework? ## conclusion this paper addresses the limitations of llms in providing personalized emotional support. we propose a self-evolution framework that enables models to learn implicit user preferences without explicit reflection. first, we use sft on esc data to equip the llm with basic emotional support skills. second, we leverage the llm's self-reflection and self-refinement capabilities to generate responses that are better aligned with the user's implicit preferences, using these pre-and post-refinement outputs as training data for iterative preference optimization. evaluations demonstrate the superiority of our framework in generating more diverse and user-aligned responses. our work advances the development of more human-centric esc systems, moving beyond formulaic empathy.