从审核到调解：LLMs能否充当在线论战中的调解员？
From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars?
AI总结：
本研究探索大型语言模型（LLMs）能否超越内容审核，作为调解员通过判断对话公平性和情感动态并生成共情缓和信息来化解在线冲突，实验表明API模型在推理和干预一致性上优于开源模型。
摘要：
大型语言模型（LLMs）的快速发展为人工智能向善应用开辟了新可能性。随着LLMs越来越多地介入在线交流，它们培养共情和建设性对话的潜力成为负责任AI研究的重要前沿。本研究探索LLMs是否不仅能作为检测有害内容的审核员，还能作为能够理解和缓和在线冲突的调解员。我们的框架将调解分解为两个子任务：判断，即LLM评估对话的公平性和情感动态；引导，即生成共情的、缓和性的信息以引导参与者走向解决。为评估调解质量，我们构建了一个大型基于Reddit的数据集，并提出了一个结合基于原则的评分、用户模拟和人工比较的多阶段评估流程。实验表明，API模型在调解时的推理和干预一致性方面优于开源模型。我们的发现突显了当前LLMs作为新兴在线社会调解代理的潜力和局限性。
英文摘要：
The rapid advancement of large language models (LLMs) has opened new possibilities for AI for good applications. As LLMs increasingly mediate online communication, their potential to foster empathy and constructive dialogue becomes an important frontier for responsible AI research. This work explores whether LLMs can serve not only as moderators that detect harmful content, but as mediators capable of understanding and de-escalating online conflicts. Our framework decomposes mediation into two subtasks: judgment, where an LLM evaluates the fairness and emotional dynamics of a conversation, and steering, where it generates empathetic, de-escalatory messages to guide participants toward resolution. To assess mediation quality, we construct a large Reddit-based dataset and propose a multi-stage evaluation pipeline combining principle-based scoring, user simulation, and human comparison. Experiments show that API-based models outperform open-source counterparts in both reasoning and intervention alignment when doing mediation. Our findings highlight both the promise and limitations of current LLMs as emerging agents for online social mediation.
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。
