从对话到自动化：利用LLM进行问题解决疗法分析
From Conversation to Automation: Leveraging LLMs for Problem-Solving Therapy Analysis
AI总结：
本文构建融合PST核心策略与促进性策略的标注框架，利用LLM和transformer模型分析真实治疗文本，发现GPT-4o策略识别准确率最高（0.76），并揭示治疗动力学与语言模式。
摘要：
问题解决疗法（PST）是一种结构化心理干预方法，通过引导个体完成问题识别、解决方案头脑风暴、决策和结果评估，帮助其管理压力并解决个人问题。随着心理健康护理越来越多地采用聊天机器人和大型语言模型（LLM）等技术，在尝试实现自动化之前，充分理解PST每次会谈如何开展至关重要。我们利用成熟的PST核心策略和一组新颖的促进性策略，开发了一个全面的PST标注框架，用于分析真实治疗对话语料库，以确定哪些策略最为普遍。通过使用多种LLM和基于transformer的模型，我们发现GPT-4o优于所有模型，在识别全部策略时取得最高准确率（0.76）。为获得更深入的洞察，我们通过分析治疗动力学（自主性、自我表露和隐喻）以及标注数据中的语言模式，考察了策略的应用方式。本研究凸显了LLM在自动化治疗对话分析方面的潜力，为心理健康干预提供了可扩展工具。我们的框架通过提升可及性、有效性以及为治疗师提供个性化支持来增强PST。
英文摘要：
Problem-solving therapy (PST) is a structured psychological approach that helps individuals manage stress and resolve personal issues by guiding them through problem identification, solution brainstorming, decision-making, and outcome evaluation. As mental health care increasingly adopts technologies like chatbots and large language models (LLMs), it is important to thoroughly understand how each session of PST is conducted before attempting to automate it. We developed a comprehensive framework for PST annotation using established PST Core Strategies and a set of novel Facilitative Strategies to analyze a corpus of real-world therapy transcripts to determine which strategies are most prevalent. Using various LLMs and transformer-based models, we found that GPT-4o outperformed all models, achieving the highest accuracy (0.76) in identifying all strategies. To gain deeper insights, we examined how strategies are applied by analyzing Therapeutic Dynamics (autonomy, self-disclosure, and metaphor), and linguistic patterns within our labeled data. Our research highlights LLMs' potential to automate therapy dialogue analysis, offering a scalable tool for mental health interventions. Our framework enhances PST by improving accessibility, effectiveness, and personalized support for therapists.
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。
