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具有重要意义但极具挑战性的任务。现有研究尝试通过大语言模型提升共情对话能力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专业知识，导致大语言模型不具备专业咨询能力。此外，如何对咨询过程中的多轮对话进行自动评估仍是尚未充分研究的领域。为弥补这一空白，我们提出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，我们设计了两阶段方法以构建高质量对话，同时开发了综合性评估基准，用于实现多轮心理咨询的有效自动评估。具有竞争力的实验结果证明了我们提出框架在心理咨询中的有效性。我们已将数据集和模型在 https://github.com/CAS-SIAT-XinHai/CPsyCoun 开源以供未来研究。