Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning

• URL: http://arxiv.org/abs/2506.11376v1
• Date: Fri, 13 Jun 2025 00:47:57 GMT
• Title: Large Language Model-Powered Conversational Agent Delivering Problem-Solving Therapy (PST) for Family Caregivers: Enhancing Empathy and Therapeutic Alliance Using In-Context Learning
• Authors: Liying Wang, Ph. D., Daffodil Carrington, M. S., Daniil Filienko, M. S., Caroline El Jazmi, M. S., Serena Jinchen Xie, M. S., Martine De Cock, Ph. D., Sarah Iribarren, Ph. D., Weichao Yuwen, Ph. D,
• Abstract summary: Family caregivers often face substantial mental health challenges.<n>This study explored the potential of a large language model (LLM)-powered conversational agent to deliver evidence-based mental health support.
• Score: 3.5944459851781057
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Reframe Your Life Story: Interactive Narrative Therapist and Innovative Moment Assessment with Large Language Models [92.93521294357058]
  Narrative therapy helps individuals transform problematic life stories into empowering alternatives.<n>Current approaches lack realism in specialized psychotherapy and fail to capture therapeutic progression over time.<n>Int (Interactive Narrative Therapist) simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate expert-like responses.
  arXiv  Detail & Related papers  (2025-07-27T11:52:09Z)
• "Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions [5.481575506447599]
  Mental health is a growing global concern, prompting interest in AI-driven solutions to expand access to psychosocial support.<n>LLMs present new opportunities to enhance peer support interactions, particularly in real-time, text-based interactions.<n>We present and evaluate an AI-supported system with an LLM-simulated distressed client, context-sensitive LLM-generated suggestions, and real-time emotion visualisations.
  arXiv  Detail & Related papers  (2025-06-11T03:06:41Z)
• Ψ-Arena: Interactive Assessment and Optimization of LLM-based Psychological Counselors with Tripartite Feedback [51.26493826461026]
  We propose Psi-Arena, an interactive framework for comprehensive assessment and optimization of large language models (LLMs)<n>Arena features realistic arena interactions that simulate real-world counseling through multi-stage dialogues with psychologically profiled NPC clients.<n>Experiments across eight state-of-the-art LLMs show significant performance variations in different real-world scenarios and evaluation perspectives.
  arXiv  Detail & Related papers  (2025-05-06T08:22:51Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions.<n>We propose a novel approach utilizing structured medical reasoning.<n>Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• From Conversation to Automation: Leveraging LLMs for Problem-Solving Therapy Analysis [6.700608883427542]
  Problem-solving therapy (PST) helps individuals manage stress and resolve personal issues.<n>It is important to thoroughly understand how each session of PST is conducted before attempting to automate it.<n>We developed a comprehensive framework for PST annotation using established PST Core Strategies and a set of novel Facilitative Strategies.
  arXiv  Detail & Related papers  (2025-01-10T16:54:20Z)
• LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
  This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment.<n>We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews.<n>Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
  arXiv  Detail & Related papers  (2025-01-07T08:49:04Z)
• SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques [9.146311285410631]
  Mental health issues significantly impact individuals' daily lives, yet many do not receive the help they need even with available online resources. This study aims to provide diverse, accessible, stigma-free, personalized, and real-time mental health support through cutting-edge AI technologies.
  arXiv  Detail & Related papers  (2024-10-17T22:04:32Z)
• CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy [67.23830698947637]
  We propose a new benchmark, CBT-BENCH, for the systematic evaluation of cognitive behavioral therapy (CBT) assistance.<n>We include three levels of tasks in CBT-BENCH: I: Basic CBT knowledge acquisition, with the task of multiple-choice questions; II: Cognitive model understanding, with the tasks of cognitive distortion classification, primary core belief classification, and fine-grained core belief classification; III: Therapeutic response generation, with the task of generating responses to patient speech in CBT therapy sessions.<n> Experimental results indicate that while LLMs perform well in reciting CBT knowledge, they fall short in complex real-world scenarios
  arXiv  Detail & Related papers  (2024-10-17T04:52:57Z)
• Therapy as an NLP Task: Psychologists' Comparison of LLMs and Human Peers in CBT [6.932239020477335]
  Large language models (LLMs) are being used as adhoc therapists.<n>We compare the session-level behaviors of human counselors with those of an LLM prompted by a team of peer counselors to deliver single-session Cognitive Behavioral Therapy.
  arXiv  Detail & Related papers  (2024-09-03T19:19:13Z)
• Toward Large Language Models as a Therapeutic Tool: Comparing Prompting Techniques to Improve GPT-Delivered Problem-Solving Therapy [6.952909762512736]
  We examine the effects of prompt engineering to guide Large Language Models (LLMs) in delivering parts of a Problem-Solving Therapy session via text. We demonstrate that the models' capability to deliver protocolized therapy can be improved with the proper use of prompt engineering methods.
  arXiv  Detail & Related papers  (2024-08-27T17:25:16Z)
• Evaluating the Efficacy of Interactive Language Therapy Based on LLM for High-Functioning Autistic Adolescent Psychological Counseling [1.1780706927049207]
  This study investigates the efficacy of Large Language Models (LLMs) in interactive language therapy for high-functioning autistic adolescents. LLMs present a novel opportunity to augment traditional psychological counseling methods.
  arXiv  Detail & Related papers  (2023-11-12T07:55:39Z)
• Building Emotional Support Chatbots in the Era of LLMs [64.06811786616471]
  We introduce an innovative methodology that synthesizes human insights with the computational prowess of Large Language Models (LLMs) By utilizing the in-context learning potential of ChatGPT, we generate an ExTensible Emotional Support dialogue dataset, named ExTES. Following this, we deploy advanced tuning techniques on the LLaMA model, examining the impact of diverse training strategies, ultimately yielding an LLM meticulously optimized for emotional support interactions.
  arXiv  Detail & Related papers  (2023-08-17T10:49:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.