LionGuard: Building a Contextualized Moderation Classifier to Tackle Localized Unsafe Content
This work addresses the need for localized moderation to handle unsafe content in low-resource languages like Singlish, though it is incremental as it applies an existing method to a new context.
The paper tackles the problem of Western-centric safety moderation in large language models by developing LionGuard, a Singapore-contextualized moderation classifier, which outperforms existing APIs by 14% on binary classification and up to 51% on multi-label tasks when tested on Singlish data.
As large language models (LLMs) become increasingly prevalent in a wide variety of applications, concerns about the safety of their outputs have become more significant. Most efforts at safety-tuning or moderation today take on a predominantly Western-centric view of safety, especially for toxic, hateful, or violent speech. In this paper, we describe LionGuard, a Singapore-contextualized moderation classifier that can serve as guardrails against unsafe LLM outputs. When assessed on Singlish data, LionGuard outperforms existing widely-used moderation APIs, which are not finetuned for the Singapore context, by 14% (binary) and up to 51% (multi-label). Our work highlights the benefits of localization for moderation classifiers and presents a practical and scalable approach for low-resource languages.