Using large language models (LLMs) to assist psychological counseling is a significant but challenging task at present. Attempts have been made on improving empathetic conversations or acting as effective assistants in the treatment with LLMs. However, the existing datasets lack consulting knowledge, resulting in LLMs lacking professional consulting competence. Moreover, how to automatically evaluate multi-turn dialogues within the counseling process remains an understudied area. To bridge the gap, we propose CPsyCoun, a report-based multi-turn dialogue reconstruction and evaluation framework for Chinese psychological counseling. To fully exploit psychological counseling reports, a two-phase approach is devised to construct high-quality dialogues while a comprehensive evaluation benchmark is developed for the effective automatic evaluation of multi-turn psychological consultations. Competitive experimental results demonstrate the effectiveness of our proposed framework in psychological counseling. We open-source the datasets and model for future research at https://github.com/CAS-SIAT-XinHai/CPsyCoun

翻译：利用大语言模型辅助心理咨询是当前一项重要但具有挑战性的任务。已有研究尝试通过大语言模型改进共情对话或使其在治疗过程中充当有效助手。然而，现有数据集缺乏咨询知识，导致大语言模型欠缺专业咨询能力。此外，如何对咨询过程中的多轮对话进行自动评估仍是一个研究不足的领域。为弥补这一差距，我们提出了CPsyCoun——一个基于报告的中文心理咨询多轮对话重构与评估框架。为充分利用心理咨询报告，我们设计了一个两阶段方法来构建高质量对话，同时开发了一个全面的评估基准，用于对多轮心理咨询进行有效的自动评估。具有竞争力的实验结果证明了我们提出的框架在心理咨询中的有效性。我们在 https://github.com/CAS-SIAT-XinHai/CPsyCoun 开源了数据集和模型，以供未来研究。