The development of AI for mental health is hindered by a lack of authentic therapy dialogues, due to strict privacy regulations and the fact that clinical sessions were historically rarely recorded. We present an LLM-driven pipeline that generates synthetic counseling dialogues based on structured client profiles and psychological questionnaires. Grounded on the principles of Cognitive Behavioral Therapy (CBT), our method creates synthetic therapeutic conversations for clinical disorders such as anxiety and depression. Our framework, SQPsych (Structured Questionnaire-based Psychotherapy), converts structured psychological input into natural language dialogues through therapist-client simulations. Due to data governance policies and privacy restrictions prohibiting the transmission of clinical questionnaire data to third-party services, previous methodologies relying on proprietary models are infeasible in our setting. We address this limitation by generating a high-quality corpus using open-weight LLMs, validated through human expert evaluation and LLM-based assessments. Our SQPsychLLM models fine-tuned on SQPsychConv achieve strong performance on counseling benchmarks, surpassing baselines in key therapeutic skills. Our findings highlight the potential of synthetic data to enable scalable, data-secure, and clinically informed AI for mental health support. We will release our code, models, and corpus at https://ai-mh.github.io/SQPsych

翻译：心理健康人工智能的发展因缺乏真实的治疗对话而受阻，这源于严格的隐私法规以及历史上临床会话鲜有记录的现实。我们提出了一种基于大语言模型的流程，能够根据结构化的患者档案与心理问卷生成合成咨询对话。基于认知行为疗法的原理，该方法可为焦虑症和抑郁症等临床障碍创建合成治疗对话。我们的框架SQPsych通过治疗师-患者模拟，将结构化心理输入转化为自然语言对话。由于数据治理政策与隐私限制禁止将临床问卷数据传输至第三方服务，以往依赖专有模型的方法在我们的场景中不可行。我们通过使用开源权重的大语言模型生成高质量语料库来突破此限制，并经由人类专家评估与大语言模型基准测试进行验证。基于SQPsychConv微调的SQPsychLLM模型在心理咨询基准测试中表现优异，在关键治疗技能上超越基线方法。我们的研究突显了合成数据在实现可扩展、数据安全且具有临床依据的心理健康支持人工智能方面的潜力。代码、模型与语料库将在https://ai-mh.github.io/SQPsych发布。