![[2512.03005] From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars?](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2512.03005] From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars?
Summary
This article explores the potential of large language models (LLMs) to act as mediators in online conflicts, moving beyond moderation to foster constructive dialogue and empathy.
Why It Matters
As online interactions become increasingly contentious, the ability of LLMs to mediate conflicts could significantly enhance digital communication. This research provides insights into how AI can be responsibly utilized to improve social interactions and reduce hostility online.
Key Takeaways
- LLMs can potentially serve as mediators by evaluating conversation dynamics and generating empathetic responses.
- A new framework decomposes mediation into judgment and steering tasks for effective conflict resolution.
- API-based models show superior performance in mediation tasks compared to open-source alternatives.
Computer Science > Artificial Intelligence arXiv:2512.03005 (cs) [Submitted on 2 Dec 2025 (v1), last revised 24 Feb 2026 (this version, v4)] Title:From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars? Authors:Dawei Li, Abdullah Alnaibari, Arslan Bisharat, Manny Sandoval, Deborah Hall, Yasin Silva, Huan Liu View a PDF of the paper titled From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars?, by Dawei Li and 6 other authors View PDF HTML (experimental) Abstract: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 r...