用大模型对话系统为照护者提供心理支持，提升共情与治疗关系。
Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning
- 通过少样本提示和检索增强生成技术，提升模型理解力。
- 参与者对模型的共情感和治疗联盟评分显著提升。
- 适合关注心理健康支持与AI应用的研究者与从业者。
家庭照护者因多重角色与资源有限，常面临严重心理挑战。本研究探索了基于大语言模型（LLM）的对话代理在为照护者提供循证心理支持方面的潜力，具体整合问题解决疗法（PST）、动机访谈（MI）与行为链分析（BCA）。一项包含28名照护者的自身对照实验中，参与者与四种LLM配置交互，评估共情与治疗联盟。表现最佳的模型采用少样本提示（Few-Shot）与检索增强生成（RAG）技术，并结合临床医生精选示例。结果显示，模型在上下文理解与个性化支持方面显著改善，体现在定性反馈与定量评分上。参与者高度认可模型在情绪验证、挖掘未表达感受及提供可操作策略方面的能力。然而，在全面评估与高效建议之间保持平衡仍是挑战。本研究展示了LLM在为家庭照护者提供富有同理心且量身定制支持方面的巨大潜力。
原文摘要 · Abstract (English)
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.
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