PsyDTCorpus 是一个由华南理工大学未来技术学院-广东省数字孪生人重点实验室于 2024 年推出的心理咨询师数字孪生数据集。该数据集的核心目标是模拟特定心理咨询师的语言风格和咨询技术，以支持心理咨询师数字孪生大模型 SoulChat2.0 的开发和训练。相关论文成果为「SoulChat: Improving LLMs’ Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations」。 PsyDTCorpus 数据集针对特定心理咨询师的真实多轮咨询案例，基于 5k 个单轮咨询样本进行数字孪生数据合成，最终得到 5k 个具有该咨询师语言风格与疗法技术应用方式的高质量心理健康对话数据。其中 4,760 个样本作为训练集，240 个样本被拆分为多个测试样例。数据集总的轮数为：90,365，其中测试集的轮次为：4,311 。 该数据集采用了一个创新的数据生成框架，该框架能够结合真实心理咨询师的语言风格、咨询技术以及来访者的大五人格特质，生成模拟单轮对话的数据。通过这个框架，研究团队能够生成多轮对话数据，这些数据有效表征了特定心理咨询师的语言风格与咨询技术应用方式。在本项目中，生成的多轮对话数据总轮次达到了 90,365 轮，平均每个对话样本包含 18 轮。 PsyDTCorpus 在谈话技术、状态与态度、关系建立、疗法技术 4 个专业维度上进行了人工评估比较，结果显示其在这些方面相较于其他数据集有明显的提升，证明了利用真实心理咨询师的少量咨询案例来构建高质量多轮心理健康对话数据的可行性。

Citation

@inproceedings{xie-etal-2025-psydt, title = “{P}sy{DT}: Using {LLM}s to Construct the Digital Twin of Psychological Counselor with Personalized Counseling Style for Psychological Counseling”, author = “Xie, Haojie and Chen, Yirong and Xing, Xiaofen and Lin, Jingkai and Xu, Xiangmin”, editor = “Che, Wanxiang and Nabende, Joyce and Shutova, Ekaterina and Pilehvar, Mohammad Taher”, booktitle = “Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)”, month = jul, year = “2025”, address = “Vienna, Austria”, publisher = “Association for Computational Linguistics”, url = “https://aclanthology.org/2025.acl-long.55/”, pages = “1081–1115”, ISBN = “979-8-89176-251-0”, 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 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.” }