构建校园心理辅导长时对话数据集，模拟学生压力随时间演化过程
Psy-Chronicle:A Structured Pipeline for Synthesizing Long-Horizon Campus Psychological Counseling Dialogues
- 基于事件图谱与双代理交互生成跨会话连续对话
- 构建含9万条对话的中文长时心理辅导数据集
- 适合研究长时记忆与因果推理的AI心理健康方向
近年来，大语言模型在心理支持任务中展现出巨大潜力。然而现有心理辅导数据多依赖单轮问答或短时多轮对话，难以刻画大学生在校园生活事件中心理困扰的积累、交互与长期演化。为此，本文提出Psy-Chronicle，一种结构化长时校园心理辅导对话生成框架。通过构建跨学期的时间压力事件图谱，建模校园压力事件间的时序顺序与演化依赖关系；结合学生代理与咨询师代理的交互仿真及结构化记忆整合机制，生成具有会话连续性的长时对话。基于该框架，我们构建并开源了CPCD——一个包含100名学生档案、90,000条辅导对话的中文长时对话数据集。同时构建CPCD-Bench，从会话级回复、长时记忆召回、时间-因果推理三个维度评估模型能力。实验表明，CPCD能有效提升相同基础架构模型的会话级回复与长时记忆召回性能，但时间-因果推理改进有限，表明事件链组织与因果解释仍是长时心理辅导建模的关键挑战。相关代码与数据已公开于：https://github.com/EdwinUSTB/Psy-Chronicle
原文摘要 · Abstract (English)
In recent years, large language models have shown substantial potential in psychological support tasks. However, existing psychological counseling data mostly rely on single-turn question answering or short multi-turn dialogues, making it difficult to characterize how college students' psychological distress accumulates, interacts, and gradually evolves over long periods within campus life events. To address this issue, this paper proposes Psy-Chronicle, a structured data-generation framework for synthesizing long-horizon campus psychological counseling dialogues. We generate a semester-spanning temporal stress event graph to model the chronological order and evolutionary dependencies among campus stress events. Through interactive simulation between a student agent and a counselor agent, together with a structured memory integration mechanism, Psy-Chronicle generates long-horizon dialogues with continuity across counseling sessions. Based on Psy-Chronicle, we construct and open-source CPCD, a Chinese long-horizon dialogue dataset for college psychological counseling, containing 100 student profiles, 90,000 counseling dialogues. We further build CPCD-Bench to evaluate models' long-horizon campus counseling capabilities from three dimensions: session-level response, long-horizon memory recall, and temporal-causal reasoning. Experimental results show that CPCD effectively improves session-level response generation and long-horizon memory recall for models with the same base architecture. Meanwhile, improvements in temporal-causal reasoning remain limited, indicating that event-chain organization and causal explanation are key challenges in long-horizon psychological counseling modeling. The related code and data are available at: https://github.com/EdwinUSTB/Psy-Chronicle
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。