PsyCLIENT: 通过对话轨迹建模实现客户端模拟，用于心理健康辅导培训和模型评估
PsyCLIENT: Client Simulation via Conversational Trajectory Modeling for Trainee Practice and Model Evaluation in Mental Health Counseling
AI总结：
PsyCLIENT通过对话轨迹建模实现客户端模拟，提供首个中文开放数据集，显著提升辅导培训和模型评估的真实性和有效性。
摘要：
基于大语言模型（LLM）的客户端模拟已成为训练初级辅导员和评估自动化辅导系统的一种有前途的工具。然而，现有的客户端模拟方法面临三个关键挑战：（1）客户端档案的多样性和真实性有限；（2）缺乏对现实客户端行为建模的系统框架；（3）中文语境下的稀缺性。为了解决这些限制，我们提出了PsyCLIENT，一种基于对话轨迹建模的新型模拟框架。通过将LLM生成过程条件化于预定义的真实世界轨迹，该轨迹结合了显式行为标签和内容约束，我们的方法确保了多样性和真实性的互动。我们进一步引入了PsyCLIENT-CP，首个开源的中文客户端档案数据集，涵盖60种不同的辅导主题。全面的评估涉及经过认证的专业辅导员，证明PsyCLIENT在真实性和培训效果方面显著优于基线方法。值得注意的是，模拟的客户端几乎无法与人类客户端区分，在辨别任务中达到约95%的专家混淆率。这些发现表明，对话轨迹建模有效地弥合了理论客户端档案与动态、真实模拟之间的差距，为心理健康教育和研究提供了稳健的解决方案。代码和数据将被释放以促进未来心理健康辅导研究。
英文摘要：
LLM-based client simulation provides a scalable approach to novice counselor training, counseling-dialogue synthesis, and interactive evaluation of automated counseling systems. However, existing approaches are limited by insufficient profile diversity, weak behavioral grounding, and the lack of open Chinese-language resources for simulated counseling clients. We propose PsyCLIENT, a framework that conditions simulated-client responses on client profiles, dialogue histories, and conversational trajectories specifying target behaviors and content constraints at each client turn. We also construct PsyCLIENT-CP, a dataset of 120 Chinese client profiles spanning 60 counseling topics. Evaluations involving 24 professional counselors show that PsyCLIENT receives higher ratings of perceived authenticity and training utility than the comparison methods. In a separate source-identification study, PsyCLIENT dialogues were more frequently misclassified as human-client interactions than those generated by the baselines. These results suggest that conversational trajectory modeling can transform static client profiles into behavior-guided, dynamically unfolding counseling interactions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
