基于大语言模型（LLM）的心理咨询具身对话智能体仿真框架
An LLM-based Simulation Framework for Embodied Conversational Agents in Psychological Counseling
AI总结：
针对心理健康合成对话数据真实性与多样性不足的问题，提出融合心理学理论的LLM基具身对话智能体（ECAs）仿真框架，扩展真实案例至认知记忆空间并生成对话，经D4数据集验证，其仿真真实性、必要性及对话质量均优于基线。
摘要：
由于隐私问题，心理健康领域的开放对话数据集主要通过人工或AI合成方法生成。然而，心理过程（尤其是来访者的心理过程）固有的内隐性，给合成数据的真实性和多样性带来挑战。本文提出ECAs（Embodied Conversational Agents，具身对话智能体的缩写）框架，这是一种基于大语言模型（LLMs）的具身智能体仿真框架，融合了多种心理学理论原则。通过仿真，我们将真实咨询案例数据扩展为细致的具身认知记忆空间，并基于高频咨询问题生成对话数据。我们使用D4数据集验证该框架：首先通过基于D4的批量仿真创建了公开的ECAs数据集；持证咨询师评估显示，该方法在仿真真实性和必要性上显著优于基线；此外，两种基于LLM的自动评估方法证实，生成对话质量高于基线。源代码和数据集可在https://github.com/AIR-DISCOVER/ECAs-Dataset获取。
英文摘要：
Due to privacy concerns, open dialogue datasets for mental health are primarily generated through human or AI synthesis methods. However, the inherent implicit nature of psychological processes, particularly those of clients, poses challenges to the authenticity and diversity of synthetic data. In this paper, we propose ECAs (short for Embodied Conversational Agents), a framework for embodied agent simulation based on Large Language Models (LLMs) that incorporates multiple psychological theoretical principles.Using simulation, we expand real counseling case data into a nuanced embodied cognitive memory space and generate dialogue data based on high-frequency counseling questions.We validated our framework using the D4 dataset. First, we created a public ECAs dataset through batch simulations based on D4. Licensed counselors evaluated our method, demonstrating that it significantly outperforms baselines in simulation authenticity and necessity. Additionally, two LLM-based automated evaluation methods were employed to confirm the higher quality of the generated dialogues compared to the baselines. The source code and dataset are available at https://github.com/AIR-DISCOVER/ECAs-Dataset.
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。
