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.

本研究解决了大型语言模型在提供问题解决疗法(PST)时的提示技术效果不足的问题。通过对提示工程的评估，展示了如何提高模型在症状识别和个性化目标设定阶段的表现，尽管存在一定限制。该研究为心理治疗的AI应用开辟了新的可能性，尤其在心理健康专业人员短缺的背景下。

大型语言模型作为治疗工具的探索：比较提示技术以改善GPT提供的问题解决疗法