hllzmz/synthetic-mental-health-convos

该数据集包含高保真度的合成患者-治疗师对话，专为心理健康领域的大型语言模型（LLMs）的监督微调（SFT）而设计。主要目标是训练AI助手从“通用知识”模型转变为具有同理心、支持性和安全意识的心理健康伴侣。对话涵盖了广泛的心理健康状况，重点关注积极倾听、心理教育和危机管理行为。数据通过复杂的教师-评判流程生成，确保了医学合理性、情感智能和严格的安全边界。

This dataset contains high-fidelity, synthetic patient-therapist dialogues designed for Supervised Fine-Tuning (SFT) of Large Language Models (LLMs) in the domain of mental health. The primary goal of this dataset is to train AI assistants to transition from general knowledge models to empathetic, supportive, and safety-conscious mental health companions. The dialogues cover a wide spectrum of mental health conditions, focusing on Active Listening, Psychoeducation, and Crisis Management behaviors. The data was generated using a sophisticated Teacher-Judge Pipeline to ensure medical plausibility, emotional intelligence, and strict safety boundaries.