首个可解释的中英双语心理辅导对话数据集，助力大模型成为有逻辑的共情助手。
Psy-Insight: Explainable Multi-turn Bilingual Dataset for Mental Health Counseling
- 构建多轮双语对话数据集，标注治疗、情绪、策略等多任务标签
- 包含逐轮推理与会话级指导，支持模型理解辅导逻辑
- 适合训练具解释性的心理咨询大模型，尤其中文场景
大型语言模型（LLMs）在情境学习方面展现出心理健康支持的巨大潜力。然而，缺乏心理辅导数据集，特别是中文语料，限制了其在该领域的应用。为此，我们构建了首个面向心理健康的可解释多任务双语数据集Psy-Insight。通过收集面对面的多轮辅导对话，并进行多任务标签及对话过程解释的标注。标注内容包括心理治疗类型、情绪状态、应对策略、话题类别，以及逐轮推理与会话级指导。Psy-Insight不仅适用于标签识别等任务，也满足训练大模型通过逻辑推理扮演同理心顾问的需求。实验表明，在Psy-Insight上训练的模型不仅能模仿对话风格，还能理解辅导背后的策略与推理过程。
原文摘要 · Abstract (English)
The in-context learning capabilities of large language models (LLMs) show great potential in mental health support. However, the lack of counseling datasets, particularly in Chinese corpora, restricts their application in this field. To address this, we constructed Psy-Insight, the first mental health-oriented explainable multi-task bilingual dataset. We collected face-to-face multi-turn counseling dialogues, which are annotated with multi-task labels and conversation process explanations. Our annotations include psychotherapy, emotion, strategy, and topic labels, as well as turn-level reasoning and session-level guidance. Psy-Insight is not only suitable for tasks such as label recognition but also meets the need for training LLMs to act as empathetic counselors through logical reasoning. Experiments show that training LLMs on Psy-Insight enables the models to not only mimic the conversation style but also understand the underlying strategies and reasoning of counseling.
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