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.

通过使用多轮对话历史记录和更接近心理咨询师表达方式的回复进行微调，可以显著增强大型语言模型在心理辅导领域的共情能力。