LionGuard 2: Building Lightweight, Data-Efficient & Localised Multilingual Content Moderators
This addresses safety gaps in multilingual moderation for Singapore, but is incremental as it builds on existing methods like pre-trained embeddings.
The paper tackled the problem of multilingual content moderation with a focus on localisation and low-resource languages, presenting LionGuard 2, a lightweight classifier that outperforms commercial and open-source systems across 17 benchmarks and is deployed in the Singapore Government.
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.