用大模型分析心理治疗对话，自动识别问题解决疗法的关键策略
From Conversation to Automation: Leveraging LLMs for Problem-Solving Therapy Analysis
- 基于核心与创新引导策略构建疗法标注框架
- GPT-4o在策略识别中准确率达76%，表现最佳
- 适合心理治疗自动化与智能辅助系统研究者
问题解决疗法（PST）是一种结构化心理干预方法，通过引导个体识别问题、头脑风暴解决方案、决策及评估结果来应对压力。随着聊天机器人和大语言模型（LLMs）在心理健康领域的应用日益广泛，理解真实治疗对话的开展方式对自动化至关重要。我们基于既有的PST核心策略和一套新提出的促进性策略，构建了完整的疗法标注框架，并用于分析真实治疗转录文本，以确定最常用的策略。采用多种LLM与基于Transformer的模型进行测试，发现GPT-4o在所有模型中表现最优，整体策略识别准确率达到0.76。为进一步揭示策略的应用模式，我们分析了治疗动态（自主性、自我披露、隐喻使用）及语言特征。研究证明，大模型具备自动化分析治疗对话的潜力，可为心理干预提供可扩展的分析工具，同时提升心理治疗的可及性、有效性与个性化支持水平。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。