用提示工程提升大模型提供心理疗法的能力，改善治疗质量。
Toward Large Language Models as a Therapeutic Tool: Comparing Prompting Techniques to Improve GPT-Delivered Problem-Solving Therapy
- 通过优化提示词设计，引导大模型更有效地执行心理治疗中的问题识别与评估。
- 专业医生评价显示，优化后的提示使治疗内容质量、一致性与共情度显著提升。
- 为缓解心理健康服务短缺，探索通用大模型在心理治疗中的实用潜力。
尽管大型语言模型（LLMs）正快速应用于包括医疗在内的多个领域，其优势与局限仍待深入研究。本研究探讨了提示工程对大模型在文本形式下执行问题解决疗法（PST）部分环节的影响，尤其聚焦于症状识别与评估阶段以实现个性化目标设定。我们通过自动评估指标及资深医疗专业人员的评审，验证了模型表现。结果表明，恰当的提示工程可显著提升通用模型交付标准化治疗的能力，尽管仍存在局限。据我们所知，这是首个系统评估多种提示技术在增强通用模型心理治疗能力方面的研究，重点关注整体质量、一致性与共情力。在心理健康专业人员严重短缺的背景下，探索大模型在心理治疗中的应用具有重要意义，有助于提升基于AI或增强型护理服务的实用性。
原文摘要 · Abstract (English)
While Large Language Models (LLMs) are being quickly adapted to many domains, including healthcare, their strengths and pitfalls remain under-explored. In our study, we examine the effects of prompt engineering to guide Large Language Models (LLMs) in delivering parts of a Problem-Solving Therapy (PST) session via text, particularly during the symptom identification and assessment phase for personalized goal setting. We present evaluation results of the models' performances by automatic metrics and experienced medical professionals. We demonstrate that the models' capability to deliver protocolized therapy can be improved with the proper use of prompt engineering methods, albeit with limitations. To our knowledge, this study is among the first to assess the effects of various prompting techniques in enhancing a generalist model's ability to deliver psychotherapy, focusing on overall quality, consistency, and empathy. Exploring LLMs' potential in delivering psychotherapy holds promise with the current shortage of mental health professionals amid significant needs, enhancing the potential utility of AI-based and AI-enhanced care services.
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