ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2505.14483
61
1

MoMoE: Mixture of Moderation Experts Framework for AI-Assisted Online Governance

20 May 2025
Agam Goyal
Xianyang Zhan
Yilun Chen
Koustuv Saha
Eshwar Chandrasekharan
    MoE
ArXiv (abs)PDFHTML
Main:9 Pages
4 Figures
Bibliography:4 Pages
2 Tables
Appendix:2 Pages
Abstract

Large language models (LLMs) have shown great potential in flagging harmful content in online communities. Yet, existing approaches for moderation require a separate model for every community and are opaque in their decision-making, limiting real-world adoption. We introduce Mixture of Moderation Experts (MoMoE), a modular, cross-community framework that adds post-hoc explanations to scalable content moderation. MoMoE orchestrates four operators -- Allocate, Predict, Aggregate, Explain -- and is instantiated as seven community-specialized experts (MoMoE-Community) and five norm-violation experts (MoMoE-NormVio). On 30 unseen subreddits, the best variants obtain Micro-F1 scores of 0.72 and 0.67, respectively, matching or surpassing strong fine-tuned baselines while consistently producing concise and reliable explanations. Although community-specialized experts deliver the highest peak accuracy, norm-violation experts provide steadier performance across domains. These findings show that MoMoE yields scalable, transparent moderation without needing per-community fine-tuning. More broadly, they suggest that lightweight, explainable expert ensembles can guide future NLP and HCI research on trustworthy human-AI governance of online communities.

View on arXiv
@article{goyal2025_2505.14483,
  title={ MoMoE: Mixture of Moderation Experts Framework for AI-Assisted Online Governance },
  author={ Agam Goyal and Xianyang Zhan and Yilun Chen and Koustuv Saha and Eshwar Chandrasekharan },
  journal={arXiv preprint arXiv:2505.14483},
  year={ 2025 }
}
Comments on this paper