PsyDT: Using LLMs to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling
Haojie Xie, Yirong Chen, Xiaofen Xing, Jingkai Lin, Xiangmin Xu
Abstract
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 styles and therapeutic types, 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 timeconsuming 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 costeffective 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 technique.Subsequently, using existing singleturn long-text dialogues with client personality, GPT-4 is guided to synthesize multi-turn dialogues of specific counselor.Finally, we finetune the LLMs on the synthesized dataset, Psy-DTCorpus, 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 realworld 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.