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LLMs learn to generate empathic compromises using similarity feedback

A new paper explores methods for generating empathic compromises between opposing viewpoints using Large Language Models. Researchers compared four prompt engineering techniques with Claude 3 Opus on a dataset of 2,400 contrasting views, finding that iterative feedback based on empathic similarity improved compromise acceptability over standard Chain of Thought reasoning. The study also involved a 50-participant evaluation and led to the training of smaller foundation models for more efficient compromise generation. AI

影响 Introduces a novel method for LLMs to generate more acceptable compromises, potentially improving human-AI collaboration in conflict resolution.

排序理由 Academic paper detailing novel methods for LLM-based compromise generation.

在 arXiv cs.CL 阅读 →

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LLMs learn to generate empathic compromises using similarity feedback

报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Sumanta Bhattacharyya, Francine Chen, Scott Carter, Yan-Ying Chen, Tatiana Lau, Nayeli Suseth Bravo, Monica P. Van, Kate Sieck, Charlene C. Wu ·

    Generating Place-Based Compromises Between Two Points of View

    arXiv:2604.24536v1 Announce Type: new Abstract: Large Language Models (LLMs) excel academically but struggle with social intelligence tasks, such as creating good compromises. In this paper, we present methods for generating empathically neutral compromises between two opposing v…

  2. arXiv cs.CL TIER_1 English(EN) · Charlene C. Wu ·

    Generating Place-Based Compromises Between Two Points of View

    Large Language Models (LLMs) excel academically but struggle with social intelligence tasks, such as creating good compromises. In this paper, we present methods for generating empathically neutral compromises between two opposing viewpoints. We first compared four different prom…