让AI根据用户性格调整共情策略，提升长期互动体验。
From Empathy to Personalized Empathy: Adapting Empathetic Strategies to Individual Users
- 基于用户历史构建个性化共情策略
- 新数据集支持细粒度共情评估，性能提升显著
- 适合研究人机共情与个性化交互的学者
随着大语言模型在长期用户互动中的广泛应用，共情能力日益重要。然而，现有研究忽略了用户人格特质对共情策略的影响。为此，我们提出个性化共情任务，旨在根据用户历史特征调整共情策略。为研究和增强该能力，我们构建了PersonaEmp数据集，涵盖长期人机交互中的丰富用户历史、人格信息及共情请求。我们进一步提出PereGRM奖励建模框架，结合共情评估结构与动态评估标准生成，实现细粒度奖励建模。在多种设置和多个评判模型下的实验结果表明，PereGRM始终表现最优，验证了其在提升个性化共情能力方面的有效性。
原文摘要 · Abstract (English)
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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