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.

翻译：我们提出一种新颖的方法——共情链（CoE）提示，该方法利用心理治疗领域的洞见，引导大语言模型（LLMs）对人类情绪状态进行推理。该方法的灵感来源于多种心理治疗流派，包括认知行为疗法（CBT）、辩证行为疗法（DBT）、来访者中心疗法（PCT）和现实疗法（RT），每种疗法均对应不同的来访者心理状态解读模式。未经过推理的LLMs主要生成探索性回应。然而，当LLMs采用共情链推理时，我们发现其共情回应范围更加全面，且与各心理治疗模型的推理模式高度吻合。基于CBT的CoE在共情回应生成方面表现出最佳的平衡性。研究结果强调了理解情绪语境及其对人类与人工智能交流影响的重要性。本项研究有助于理解如何将心理治疗模型融入LLMs，从而推动开发更具情境特异性、更安全且更具共情能力的人工智能系统。