发表机构
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有心理支持对话数据生成方法的局限，提出EmoTrace框架，通过构建求助者认知概况及相关模块，以模拟其情感轨迹，增强情感表达层次性，实验证明该方法在情感丰富度和共情质量上优于现有方法。
AI 中文摘要
使用大语言模型辅助心理咨询是自然语言处理领域的一项重要任务。高质量心理支持对话语料库的构建是训练面向咨询的对话模型的关键基础。然而，现有数据生成方法存在情感稳定的求助者、情感动态变化有限以及高度遵循咨询师指导等局限性，导致大语言模型缺乏有效应对情绪不稳定场景的能力。此外，咨询师的回复通常由解决问题的目标驱动，忽视了以情感为中心的互动作用。为填补这些空白，我们提出EmoTrace，一个以模拟求助者情感轨迹为中心的多轮对话语料库生成框架。我们构建了求助者的认知概况，引入了带有情感模式和相关激活机制的求助者模块、咨询师模块和情感轨迹控制模块，增强了求助者情感表达和咨询师针对性共情表达的层次性。实验结果表明，该方法在情感丰富度和共情质量方面优于现有方法。
英文摘要
Using large language models (LLMs) to assist psychological counseling is an important task in the field of natural language processing. The construction of high-quality psychological support dialogue corpora serves as a critical foundation for training counseling-oriented conversational models. However, existing data generation approaches generally suffer from several limitations, including emotionally stable seekers, limited variation in emotional dynamics, and a high degree of compliance with counselors' guidance. These issues result in LLM that lack the capability to effectively respond to emotionally unstable scenarios. In addition, counselor responses are typically driven by problem-solving objectives, thereby overlooking the role of emotion-focused interaction, which are essential in psychological counseling. To address these gaps, we propose EmoTrace, a multi-turn dialogue corpus generation framework centered on modeling seekers' emotional trajectories. we construct seekers' cognitive profile and introduce a seeker module with emotional schemas and an associated activation mechanism, a counselor module, and an emotional trajectory control module, thereby enhancing the layering of the seeker's emotional expression and the counselor's targeted empathic expression. Experimental results demonstrate that the proposed method outperforms existing approaches in terms of emotional richness and empathy quality.
Comments36 pages, 20 figures
