Currently, large language models (LLMs) have made significant progress in the field of psychological counseling. However, existing mental health LLMs overlook a critical issue where they do not consider the fact that different psychological counselors exhibit different personal styles, including linguistic style and therapy techniques, etc. As a result, these LLMs fail to satisfy the individual needs of clients who seek different counseling styles. To help bridge this gap, we propose PsyDT, a novel framework using LLMs to construct the Digital Twin of Psychological counselor with personalized counseling style. Compared to the time-consuming and costly approach of collecting a large number of real-world counseling cases to create a specific counselor's digital twin, our framework offers a faster and more cost-effective solution. To construct PsyDT, we utilize dynamic one-shot learning by using GPT-4 to capture counselor's unique counseling style, mainly focusing on linguistic style and therapy techniques. Subsequently, using existing single-turn long-text dialogues with client's questions, GPT-4 is guided to synthesize multi-turn dialogues of specific counselor. Finally, we fine-tune the LLMs on the synthetic dataset, PsyDTCorpus, to achieve the digital twin of psychological counselor with personalized counseling style. Experimental results indicate that our proposed PsyDT framework can synthesize multi-turn dialogues that closely resemble real-world counseling cases and demonstrate better performance compared to other baselines, thereby show that our framework can effectively construct the digital twin of psychological counselor with a specific counseling style.

翻译：当前，大语言模型（LLMs）在心理咨询领域已取得显著进展。然而，现有的心理健康大语言模型忽视了一个关键问题：它们未考虑不同心理咨询师展现出不同个人风格（包括语言风格和治疗技术等）这一事实。因此，这些模型无法满足寻求不同咨询风格的来访者的个性化需求。为弥补这一差距，我们提出PsyDT，一种利用大语言模型构建具有个性化咨询风格的心理咨询师数字孪生的新型框架。相较于收集大量真实世界咨询案例以创建特定咨询师数字孪生这种耗时且成本高昂的方法，我们的框架提供了一种更快速、更具成本效益的解决方案。为构建PsyDT，我们采用动态单样本学习方法，利用GPT-4捕捉咨询师独特的咨询风格，主要聚焦于语言风格和治疗技术。随后，利用现有的来访者提问单轮长文本对话，引导GPT-4合成特定咨询师的多轮对话。最后，我们在合成数据集PsyDTCorpus上对大语言模型进行微调，以实现具有个性化咨询风格的心理咨询师数字孪生。实验结果表明，我们提出的PsyDT框架能够合成与真实世界咨询案例高度相似的多轮对话，并展现出优于其他基线模型的性能，从而证明该框架能够有效构建具有特定咨询风格的心理咨询师数字孪生。