We present a novel method, the Chain of Empathy (CoE) prompting, that
utilizes insights from psychotherapy to induce Large Language Models (LLMs) to
reason about human emotional states. This method is inspired by various
psychotherapy approaches including Cognitive Behavioral Therapy (CBT),
Dialectical Behavior Therapy (DBT), Person Centered Therapy (PCT), and Reality
Therapy (RT), each leading to different patterns of interpreting clients'
mental states. LLMs without reasoning generated predominantly exploratory
responses. However, when LLMs used CoE reasoning, we found a more comprehensive
range of empathetic responses aligned with the different reasoning patterns of
each psychotherapy model. The CBT based CoE resulted in the most balanced
generation of empathetic responses. The findings underscore the importance of
understanding the emotional context and how it affects human and AI
communication. Our research contributes to understanding how psychotherapeutic
models can be incorporated into LLMs, facilitating the development of
context-specific, safer, and empathetic AI.

我们提出了一种新颖的方法 —— 共情链（CoE）提示法，利用来自心理治疗学的见解来引导大型语言模型（LLM）对人类的情感状态进行推理。通过采用认知行为治疗（CBT）、辩证行为治疗（DBT）、人本主义治疗（PCT）和现实疗法（RT）等不同心理治疗方法，我们发现 LLM 在没有推理的情况下主要产生探索性的回应，而当 LLM 使用共情链推理时，我们发现其生成了更全面的同理回应，与每种心理治疗模型的不同推理模式相一致。CBT 基于的 CoE 方法生成了最平衡的同理回应，该研究强调了理解情感背景及其如何影响人类和人工智能沟通的重要性。我们的研究有助于了解将心理治疗模型纳入 LLM 的方式，促进特定环境、更安全和富有同理心的人工智能的发展。