Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
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.
