Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models

TL;DR

This paper introduces the Chain of Empathy prompting method, inspired by psychotherapy models, to improve the empathetic reasoning and responses of large language models, making them more context-aware and emotionally intelligent.

Contribution

It presents a novel CoE prompting approach that incorporates psychotherapy insights to enhance LLMs' empathetic reasoning and response generation.

Findings

CoE prompts lead to more empathetic responses aligned with psychotherapy patterns

CBT-based CoE produces the most balanced empathetic responses

Psychotherapy-inspired prompts improve LLMs' understanding of emotional context

Abstract

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.