Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models
This work addresses the need for more empathetic AI in communication, though it is incremental in applying existing psychotherapy insights to LLMs.
The authors tackled the problem of generating empathetic responses from large language models by introducing Chain of Empathy prompting, which uses psychotherapy models to reason about emotional states, resulting in more comprehensive and balanced empathetic responses, with CBT-based CoE performing best.
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.