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. 1

Multi-intent spoken language understanding (SLU) that can handle multiple intents in an utterance has attracted increasing attention. Previous studies treat the slot filling task as a token-level sequence labeling task, which results in a lack of entity-related information. In our paper, we propose anEntity-LevelSlotFilling (ELSF) framework for joint multiple intent detection and slot filling. In our framework, two entity-oriented auxiliary tasks, entity boundary detection and entity type assignment, are introduced as the regularization to capture the entity boundary and the context of type, respectively. Besides, to better utilize the entity interaction, we design an effective entity-level coordination mechanism for modeling the interaction in both entity-entity and intent-entity relationships. Experiments on five datasets demonstrate the effectiveness and generalizability of our ELSF.