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.

翻译：大型语言模型（LLM）在识别在线社区有害内容方面展现出巨大潜力。然而，现有的审核方法需要为每个社区单独训练模型，且决策过程不透明，限制了实际应用。本文提出混合审核专家框架（MoMoE），这是一个模块化的跨社区框架，可为可扩展的内容审核提供事后解释。MoMoE通过四个操作算子——分配、预测、聚合、解释——进行协调，并具体实现为七类社区专用专家（MoMoE-Community）与五类规范违反专家（MoMoE-NormVio）。在30个未见过的Reddit子社区测试中，最优变体分别取得了0.72和0.67的Micro-F1分数，达到或超越了强微调基线模型的表现，同时持续生成简洁可靠的可解释性输出。尽管社区专用专家能实现最高的峰值准确率，但规范违反专家在不同领域表现出更稳定的性能。这些结果表明，MoMoE能够在不需针对每个社区进行微调的情况下，实现可扩展且透明的审核机制。更广泛而言，本研究说明轻量级、可解释的专家集成方法可为未来自然语言处理与人机交互领域关于在线社区可信人机协同治理的研究提供指引。