TWGuard: A Case Study of LLM Safety Guardrails for Localized Linguistic Contexts
For regional communities deploying LLMs, this work demonstrates a method to adapt safety guardrails to local linguistic contexts, addressing a gap in current AI safety research.
The paper addresses the lack of linguistic and cultural context in LLM safety guardrails by optimizing a guardrail model for Taiwan's linguistic context, achieving a +0.289 F1 gain over the foundation model and a 94.9% reduction in false positive rate compared to the strongest baseline.
Safety guardrails have become an active area of research in AI safety, aimed at ensuring the appropriate behavior of large language models (LLMs). However, existing research lacks consideration of nuances across linguistic and cultural contexts, resulting in a gap between reported performance and in-the-wild effectiveness. To address this issue, this paper proposes an approach to optimize guardrail models for a designated linguistic context by leveraging a curated dataset tailored to local linguistic characteristics, targeting the Taiwan linguistic context as a representative example of localized deployment challenges. The proposed approach yields TWGuard, a linguistic context-optimized guardrail model that achieves a huge gain (+0.289 in F1) compared to the foundation model and significantly outperforms the strongest baseline in practical use (-0.037 in false positive rate, a 94.9\% reduction). Together, this work lays a foundation for regional communities to establish AI safety standards grounded in their own linguistic contexts, rather than accepting boundaries imposed by dominant languages. The inadequacy of the latter is reconfirmed by our findings.