共情链：基于心理治疗模型增强大语言模型的共情回应能力
Chain of Empathy: Enhancing Empathetic Response of Large Language Models Based on Psychotherapy Models
AI总结：
本研究提出受多类心理治疗模型启发的共情链（CoE）提示方法，可引导大语言模型生成更丰富均衡的共情回应，为开发更安全、具语境适配性的共情AI提供思路。
摘要：
我们提出了一种名为共情链（Chain of Empathy, CoE）提示的新方法，该方法利用心理治疗领域的洞见，引导大语言模型（Large Language Models, LLMs）对人类情绪状态进行推理。该方法受多种心理治疗流派启发，包括认知行为疗法（Cognitive Behavioral Therapy, CBT）、辩证行为疗法（Dialectical Behavior Therapy, DBT）、以人为中心疗法（Person Centered Therapy, PCT）和现实疗法（Reality Therapy, RT），每种流派对应不同的来访者心理状态解读模式。未采用推理模式的LLM主要生成探索性回应，而当LLM使用CoE推理时，我们观察到更全面的共情回应类型，且与各心理治疗模型的不同推理模式相匹配，其中基于CBT的CoE生成的共情回应最为均衡。研究结果凸显了理解情绪语境及其对人机交流影响的重要性。本研究有助于探索如何将心理治疗模型融入LLM，推动开发适配特定语境、更安全且具共情能力的AI。
英文摘要：
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.
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。
