# From Moderation to Mediation: Can LLMs Serve as Mediators in Online Flame Wars?

arXiv: https://arxiv.org/abs/2512.03005 · HTML: https://mlsanity.com/paper/2512.03005/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge](https://mlsanity.com/paper/2411.16594/) (2024)
- [BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://mlsanity.com/paper/2402.10373/) (2024)
- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)
- [Human-like Summarization Evaluation with ChatGPT](https://mlsanity.com/paper/2304.02554/) (2023)
- [ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs](https://mlsanity.com/paper/2305.15964/) (2023)
- [A Large Language Model Approach to Educational Survey Feedback Analysis](https://mlsanity.com/paper/2309.17447/) (2023)
- [Tree-Based Representation and Generation of Natural and Mathematical Language](https://mlsanity.com/paper/2302.07974/) (2023)
- [Multilingual Content Moderation: A Case Study on Reddit](https://mlsanity.com/paper/2302.09618/) (2023)
- [ESC-Judge: A Framework for Comparing Emotional Support Conversational Agents](https://mlsanity.com/paper/2505.12531/) (2025)
- [Investigating the Efficacy of Large Language Models in Reflective Assessment Methods through Chain of Thoughts Prompting](https://mlsanity.com/paper/2310.00272/) (2023)

## Built on by (most cited first, 3 of 3)

- [Student Perceptions of Large Language Models Use in Self-Reflection and Design Critique in Architecture Studio](https://mlsanity.com/paper/2602.00041/) (2026)
- [LLMs Should Incorporate Explicit Mechanisms for Human Empathy](https://mlsanity.com/paper/2604.10557/) (2026)
- [CC-Mediation: Evaluating Large Language Models for Cross-Cultural Conflict Mediation](https://mlsanity.com/paper/2609.04855/) (2026)

## Abstract (arXiv)

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.