Psy-Chronicle: 一个用于合成长周期校园心理辅导对话的结构化流水线
Psy-Chronicle:A Structured Pipeline for Synthesizing Long-Horizon Campus Psychological Counseling Dialogues
AI总结：
本文提出Psy-Chronicle，一种结构化数据生成框架，用于合成长周期校园心理辅导对话，通过生成学期跨度的时间压力事件图和学生与辅导员代理的交互模拟，构建了包含100个学生档案和9万条对话的CPCD数据集，并通过CPCD-Bench评估模型的长周期校园辅导能力，实验结果表明CPCD有效提升了模型的会话级响应生成和长周期记忆召回能力。
摘要：
近年来，大语言模型在心理支持任务中展现出显著潜力。然而，现有心理辅导数据大多依赖于单轮问答或短多轮对话，难以刻画大学生心理困扰在校园生活事件中如何积累、交互并逐渐演变的长期过程。为解决这一问题，本文提出Psy-Chronicle，一种结构化数据生成框架，用于合成长周期校园心理辅导对话。我们生成一个跨越学期的时间压力事件图，以建模校园压力事件之间的时序顺序和演变依赖关系。通过学生代理与辅导员代理之间的交互模拟，以及结构化记忆整合机制，Psy-Chronicle生成具有连续性且跨越多个辅导会话的长周期对话。基于Psy-Chronicle，我们构建并开源了CPCD，一个中文长周期对话数据集，包含100个学生档案和90,000条辅导对话。我们进一步构建CPCD-Bench，从三个维度评估模型的长周期校园辅导能力：会话级响应、长周期记忆召回和时间因果推理。实验结果表明，CPCD有效提升了具有相同基础架构的模型的会话级响应生成和长周期记忆召回能力。同时，时间因果推理的改进仍有限，表明事件链组织和因果解释是长周期心理辅导建模中的关键挑战。相关代码和数据可在：https://github.com/EdwinUSTB/Psy-Chronicle 获取。
英文摘要：
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
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。
