搭载大语言模型的对话式智能体为家庭照护者提供问题解决疗法（PST）：利用上下文学习提升共情能力与治疗联盟
Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning
AI总结：
本研究针对家庭照护者的心理健康需求，开发整合PST、MI与BCA的LLM对话智能体，通过少样本和RAG技术提升共情与治疗联盟效果，验证了LLM在该场景的应用潜力与现存挑战。
摘要：
家庭照护者因角色多元且资源有限，常面临严峻的心理健康挑战。本研究探讨了大语言模型（LLM）驱动的对话式智能体为照护者提供循证心理健康支持的潜力，具体为整合了动机式访谈（MI）和行为链分析（BCA）的问题解决疗法（PST）。研究开展了一项被试内实验，招募28名照护者与4种LLM配置交互，以评估共情能力与治疗联盟水平。表现最佳的模型结合了少样本（Few-Shot）和检索增强生成（RAG）提示技术，以及临床医生 curated 的示例。这些模型展现出更强的上下文理解能力与个性化支持效果，这一点从质性反馈以及感知共情、治疗联盟的量化评分中得到体现。参与者认可模型在情绪确认、挖掘未表达感受及提供可行策略方面的能力。不过，如何在全面评估与高效提供建议之间取得平衡仍是一项挑战。本研究凸显了LLM为家庭照护者提供共情性、个性化支持的潜力。
英文摘要：
Family caregivers often face substantial mental health challenges due to their multifaceted roles and limited resources. This study explored the potential of a large language model (LLM)-powered conversational agent to deliver evidence-based mental health support for caregivers, specifically Problem-Solving Therapy (PST) integrated with Motivational Interviewing (MI) and Behavioral Chain Analysis (BCA). A within-subject experiment was conducted with 28 caregivers interacting with four LLM configurations to evaluate empathy and therapeutic alliance. The best-performing models incorporated Few-Shot and Retrieval-Augmented Generation (RAG) prompting techniques, alongside clinician-curated examples. The models showed improved contextual understanding and personalized support, as reflected by qualitative responses and quantitative ratings on perceived empathy and therapeutic alliances. Participants valued the model's ability to validate emotions, explore unexpressed feelings, and provide actionable strategies. However, balancing thorough assessment with efficient advice delivery remains a challenge. This work highlights the potential of LLMs in delivering empathetic and tailored support for family caregivers.
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。
