迈向真实世界的中文心理支持对话：CPsDD数据集与协同进化多智能体系统
Toward Real-World Chinese Psychological Support Dialogues: CPsDD Dataset and a Co-Evolving Multi-Agent System
AI总结：
针对中文心理支持数据集稀缺问题，提出微调大模型生成数据并构建CPsDD数据集，同时开发协同进化多智能体系统CADSS，在策略预测和ESC任务上达到最优性能。
摘要：
由于压力不断增加，对心理支持的需求日益增长，这暴露了相关数据集的稀缺性，尤其是在非英语语言方面。为了解决这个问题，我们提出了一个框架，利用有限的真实世界数据和专家知识来微调两个大型语言模型：Dialog Generator和Dialog Modifier。Generator基于预定义路径创建大规模的心理咨询对话，这些路径指导系统响应策略和用户交互，构成有效支持的基础。Modifier对这些对话进行细化，使其符合真实世界的数据质量。通过自动和人工审查，我们构建了中文心理支持对话数据集（CPsDD），包含13个群体、16种心理问题、13种原因和12种支持重点的68K对话。此外，我们引入了综合智能体对话支持系统（CADSS），其中Profiler分析用户特征，Summarizer压缩对话历史，Planner选择策略，Supporter生成共情响应。策略预测和情感支持对话（ESC）任务的实验结果表明，CADSS在CPsDD和ESConv数据集上均达到了最先进的性能。
英文摘要：
The growing need for psychological support due to increasing pressures has exposed the scarcity of relevant datasets, particularly in non-English languages. To address this, we propose a framework that leverages limited real-world data and expert knowledge to fine-tune two large language models: Dialog Generator and Dialog Modifier. The Generator creates large-scale psychological counseling dialogues based on predefined paths, which guide system response strategies and user interactions, forming the basis for effective support. The Modifier refines these dialogues to align with real-world data quality. Through both automated and manual review, we construct the Chinese Psychological support Dialogue Dataset (CPsDD), containing 68K dialogues across 13 groups, 16 psychological problems, 13 causes, and 12 support focuses. Additionally, we introduce the Comprehensive Agent Dialogue Support System (CADSS), where a Profiler analyzes user characteristics, a Summarizer condenses dialogue history, a Planner selects strategies, and a Supporter generates empathetic responses. The experimental results of the Strategy Prediction and Emotional Support Conversation (ESC) tasks demonstrate that CADSS achieves state-of-the-art performance on both CPsDD and ESConv datasets.
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。
