发表机构
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究探究LLMs能否模仿Reddit社区用户的交流风格，发现其在复制评论结构与正式度上表现良好，但捕捉细微情绪存在不足，为引导在线对话亲社会化提供了方向。
AI 中文摘要
在线社区长期面临对抗有害内容与错误信息的挑战，人工版主难以应对海量内容，而大型语言模型（LLMs）为自动生成建设性回复、塑造在线交互提供了有前景的解决方案。本文初步探究LLMs能否以Reddit用户的评论历史为上下文，模仿其交流风格，评估了两种提示方法：预测目标评论和填充掩码评论。研究发现，LLMs在复制评论结构与正式度方面超出预期，但难以准确捕捉细微情绪，例如低估喜悦情绪、高估愤怒情绪。这些发现凸显了LLMs在引导在线对话走向亲社会方向的潜力，可影响社区内涌现的交流模式与规范；本研究结果为后续更严格评估方法的研究提供了启发，以探索LLMs在不同在线社区的有效性，更好地理解其更广泛的社会影响。
英文摘要
Online communities face a constant battle against toxicity and misinformation. While human moderators struggle to keep pace with the volume of content, LLMs offer a promising solution for automatically generating constructive responses and shaping online interactions. This paper preliminarily investigates if LLMs can mimic the communication styles of Reddit users using their comment history as context. We evaluate two prompting approaches: predicting a target comment and filling in masked comments. We find that LLMs outperform expectations at replicating comment structure and formality, but struggle to accurately capture nuanced emotions, e.g. understating joy and overstating anger. These findings highlight a promising direction for LLMs in guiding online conversations towards prosociality influencing emergent communication patterns and norms within the community. The results of our study inspire future work with more rigorous methods of evaluation to explore the LLMs' effectiveness across diverse online communities to better understand their broader societal impact.
Comments5 pages, 2 tables, 1 figure
