SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities
  through Fine-tuning with Multi-turn Empathy Conversations
Yirong Chen Xiaofen Xing Jingkai Lin Huimin Zheng Zhenyu Wang Qi Liu Xiangmin Xu
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.