从共情到个性化共情：根据个体用户调整共情策略
From Empathy to Personalized Empathy: Adapting Empathetic Strategies to Individual Users
AI总结：
针对大语言模型长期交互中忽略用户个性对共情策略影响的问题，提出个性化共情任务，构建PersonaEmp数据集和PereGRM奖励建模框架，实验证明其有效提升个性化共情能力。
摘要：
随着大语言模型（LLMs）越来越多地部署在与用户的长期交互中，共情已成为一项日益重要的能力。然而，现有研究忽视了用户个性特征对长期交互中共情策略的影响。为弥补这一空白，我们引入了个性化共情任务，其重点是根据从历史中获得的用户个性化特征调整共情策略。为了研究和增强这一能力，我们构建了PersonaEmp，一个基于长期用户-AI交互构建的个性化共情数据集，具有丰富的用户历史、人物信息和寻求共情的查询。我们进一步提出了PereGRM，一种奖励建模框架，它将共情评估结构与动态评估标准生成相结合，用于细粒度奖励建模。在不同设置和多个评判模型下的实验结果表明，PereGRM始终取得最强的性能提升，表明其在增强个性化共情能力方面的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
