Large language models (LLMs) have been widely applied in various fields due to their excellent capability for memorizing knowledge and chain of thought (CoT). When these language models are applied in the field of psychological counseling, they often rush to provide universal advice. However, when users seek psychological support, they need to gain empathy, trust, understanding and comfort, rather than just reasonable advice. To this end, we constructed a multi-turn empathetic conversation dataset of more than 2 million samples, in which the input is the multi-turn conversation context, and the target is empathetic responses that cover expressions such as questioning, comfort, recognition, listening, trust, emotional support, etc. Experiments have shown that the empathy ability of LLMs can be significantly enhanced when finetuning by using multi-turn dialogue history and responses that are closer to the expression of a psychological consultant.

翻译：大语言模型凭借其卓越的知识记忆与思维链能力，已在多个领域得到广泛应用。然而当这些语言模型应用于心理咨询领域时，往往倾向于直接给出通用性建议。事实上，用户在寻求心理支持时，更需要获得共情、信任、理解与抚慰，而不仅仅是合理的建议。为此，我们构建了一个包含超过200万样本的多轮共情对话数据集，其中输入为多轮对话上下文，目标输出则涵盖提问、抚慰、认同、倾听、信任、情感支持等表达方式的共情回应。实验表明，采用更接近心理咨询师表达方式的多轮对话历史与回复进行微调时，大语言模型的共情能力可获得显著提升。