arXiv:2606.00728v1 Announce Type: new 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 personalize
The increasing deployment of LLMs in long-term user interactions necessitates more sophisticated and personalized human-computer interfaces to enhance engagement and utility.
Adapting empathetic strategies based on individual user characteristics can significantly improve the efficacy and acceptance of AI systems, moving beyond generic interactions to more nuanced and effective engagement.
AI models will move from generalized empathetic responses to highly personalized ones, learning and adapting to individual users' historical interactions and personality traits.
- · AI developers
- · Customer service industries
- · Mental health tech
- · Personalized learning platforms
- · Generic chatbot providers
- · Platforms with non-adaptive AI
- · Low-empathy AI applications
LLMs provide more contextually appropriate and effective responses, increasing user satisfaction and retention.
The ability to personalize empathy could lead to deeper human-AI relationships and a blurring of lines between human and AI interaction.
Ethical considerations around manipulation and data privacy will intensify as AI learns and leverages individual psychological profiles.
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