This report presents the Empathetic Cascading Networks (ECN) framework, a multi-stage prompting method designed to enhance the empathetic and inclusive capabilities of large language models. ECN employs four stages: Perspective Adoption, Emotional Resonance, Reflective Understanding, and Integrative Synthesis, to guide models toward generating emotionally resonant and contextually aware responses. Experimental results demonstrate that ECN achieves the highest Empathy Quotient (EQ) scores across GPT-3.5-turbo and GPT-4, while maintaining competitive Regard and Perplexity metrics. These findings emphasize ECN's potential for applications requiring empathy and inclusivity in conversational AI.

翻译：本报告提出了共情级联网络（ECN）框架，这是一种旨在增强大型语言模型共情与包容能力的多阶段提示方法。ECN采用四个阶段：视角采纳、情感共鸣、反思性理解与整合性综合，以引导模型生成情感共鸣且情境感知的回应。实验结果表明，ECN在GPT-3.5-turbo和GPT-4上均取得了最高的共情商数（EQ）得分，同时保持了具有竞争力的尊重度与困惑度指标。这些发现凸显了ECN在对话式人工智能中需要共情与包容的应用潜力。