PsyDT: Using LLMs to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling

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 techniques. Subsequently, using existing singleturn long-text dialogues with client’s questions, GPT-4 is guided to synthesize multi-turn dialogues of specific counselor. Finally, we finetune the LLMs on the synthetic 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,