迈向作为治疗工具的大语言模型：比较提示技术以改进GPT提供的问题解决疗法
Toward Large Language Models as a Therapeutic Tool: Comparing Prompting Techniques to Improve GPT-Delivered Problem-Solving Therapy
AI总结：
本研究比较多种提示工程技术对GPT提供问题解决疗法的影响，发现恰当的提示工程能提升模型在症状识别与目标设定阶段的治疗质量、一致性和共情能力，为AI辅助心理治疗提供了实证依据。
摘要：
尽管大语言模型（LLMs）正被迅速应用于包括医疗保健在内的许多领域，但其优势与缺陷仍未得到充分探索。在我们的研究中，我们考察了提示工程在引导大语言模型（LLMs）通过文本提供问题解决疗法（Problem-Solving Therapy, PST）部分会话内容时的效果，特别是在用于个性化目标设定的症状识别与评估阶段。我们展示了通过自动指标和经验丰富的医学专业人员对模型性能的评估结果。我们证明，通过恰当使用提示工程方法，模型提供规范化治疗的能力可以得到提升，尽管仍存在局限性。据我们所知，本研究是首批评估各种提示技术在增强通用模型提供心理治疗能力方面效果的研究之一，重点关注整体质量、一致性和共情能力。在当前心理健康专业人员短缺而需求巨大的背景下，探索大语言模型在提供心理治疗方面的潜力具有广阔前景，有助于提升基于人工智能和人工智能增强型护理服务的潜在效用。
英文摘要：
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.
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。
