用大模型提升网络讨论质量，防偏激促理性
Evaluation and Facilitation of Online Discussions in the LLM Era: A Survey
- 构建对话质量评估新分类体系
- 提出大模型辅助引导讨论的策略框架
- 整理专用数据集并给出未来研究路线
本文综述了在大语言模型时代评估与提升在线讨论质量的方法。尽管在线讨论理论上旨在促进相互理解，但常演变为仇恨言论等有害交流，威胁社会凝聚力与民主价值。近年来大模型的发展使人工智能代理不仅能内容审核，还能主动优化互动质量。本综述融合自然语言处理与社会科学视角，提出（a）讨论质量评估的新分类体系，（b）干预与引导策略概览，（c）对话引导数据集的新分类，（d）面向大模型的技术与社会双重视角的实践路径与未来研究方向。
原文摘要 · Abstract (English)
We present a survey of methods for assessing and enhancing the quality of online discussions, focusing on the potential of LLMs. While online discourses aim, at least in theory, to foster mutual understanding, they often devolve into harmful exchanges, such as hate speech, threatening social cohesion and democratic values. Recent advancements in LLMs enable artificial facilitation agents to not only moderate content, but also actively improve the quality of interactions. Our survey synthesizes ideas from NLP and Social Sciences to provide (a) a new taxonomy on discussion quality evaluation, (b) an overview of intervention and facilitation strategies, (c) along with a new taxonomy of conversation facilitation datasets, (d) an LLM-oriented roadmap of good practices and future research directions, from technological and societal perspectives.
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