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[2511.18696] Empathetic Cascading Networks: A Multi-Stage Prompting Technique for Reducing Social Biases in Large Language Models
Summary
The paper presents Empathetic Cascading Networks (ECN), a multi-stage prompting technique aimed at enhancing the empathetic responses of large language models, demonstrating improved empathy and inclusivity metrics.
Why It Matters
As AI language models become increasingly integrated into social applications, addressing biases and enhancing empathetic communication is crucial. This research offers a structured approach to improve the emotional intelligence of AI, which can lead to more inclusive and effective interactions in various contexts.
Key Takeaways
- ECN employs four stages: Perspective Adoption, Emotional Resonance, Reflective Understanding, and Integrative Synthesis.
- The technique significantly improves Empathy Quotient (EQ) scores in models like GPT-3.5-turbo and GPT-4.
- Maintains competitive performance in Regard and Perplexity metrics alongside enhanced empathy.
- Highlights the importance of empathetic AI in applications requiring sensitive and context-aware communication.
- The research underscores the potential for reducing social biases in AI through structured prompting.
Computer Science > Computation and Language arXiv:2511.18696 (cs) This paper has been withdrawn by Wangjiaxuan Xin [Submitted on 24 Nov 2025 (v1), last revised 19 Feb 2026 (this version, v2)] Title:Empathetic Cascading Networks: A Multi-Stage Prompting Technique for Reducing Social Biases in Large Language Models Authors:Wangjiaxuan Xin View a PDF of the paper titled Empathetic Cascading Networks: A Multi-Stage Prompting Technique for Reducing Social Biases in Large Language Models, by Wangjiaxuan Xin No PDF available, click to view other formats Abstract: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. Comments: Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI) Cite as: arXiv:2511.18696 [cs.CL] (or arXiv:2511.18696v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2511.18696 Focus to learn more ...
