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arXiv · 2507.15339

LionGuard 2: Building Lightweight, Data-Efficient & Localised Multilingual Content Moderators

Leanne Tan·Gabriel Chua·Ziyu Ge·Roy Ka-Wei Lee
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Abstract

Modern moderation systems increasingly support multiple languages, but often fail to address localisation and low-resource variants - creating safety gaps in real-world deployments. Small models offer a potential alternative to large LLMs, yet still demand considerable data and compute. We present LionGuard 2, a lightweight, multilingual moderation classifier tailored to the Singapore context, supporting English, Chinese, Malay, and partial Tamil. Built on pre-trained OpenAI embeddings and a multi-head ordinal classifier, LionGuard 2 outperforms several commercial and open-source systems across 17 benchmarks, including both Singapore-specific and public English datasets. The system is actively deployed within the Singapore Government, demonstrating practical efficacy at scale. Our findings show that high-quality local data and robust multilingual embeddings can achieve strong moderation performance, without fine-tuning large models. We release our model weights and part of our training data to support future work on LLM safety.

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Leanne Tan, Gabriel Chua, Ziyu Ge, Roy Ka-Wei Lee. 2025-07-21. LionGuard 2: Building Lightweight, Data-Efficient & Localised Multilingual Content Moderators. https://arxiv.org/abs/2507.15339

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