SoulChat：通过多轮共情对话微调提升大语言模型的共情、倾听与安慰能力
SoulChat: Improving LLMs' Empathy, Listening, and Comfort Abilities through Fine-tuning with Multi-turn Empathy Conversations
AI总结：
针对LLMs在心理咨询中急于给建议而缺乏共情的问题，本文构建了超200万样本的多轮共情对话数据集，并通过微调显著提升了模型的共情、倾听与安慰能力。
摘要：
大语言模型（LLMs）因其出色的知识记忆和思维链（CoT）能力，已在多个领域得到广泛应用。当这些语言模型应用于心理咨询领域时，它们往往急于提供通用建议。然而，当用户寻求心理支持时，他们需要获得共情、信任、理解和安慰，而不仅仅是合理的建议。为此，我们构建了一个包含超过200万个样本的多轮共情对话数据集，其中输入为多轮对话上下文，目标为涵盖询问、安慰、认可、倾听、信任、情感支持等表达的共情回应。实验表明，通过使用多轮对话历史以及更接近心理咨询师表达方式的回应进行微调，LLMs的共情能力可以得到显著增强。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
