SEA-Guard: Culturally Grounded Multilingual Safeguard for Southeast Asia
Panuthep Tasawong, Jian Gang Ngui, Alham Fikri Aji, Trevor Cohn, Peerat Limkonchotiwat

TL;DR
SEA-Guard introduces a culturally grounded multilingual safeguard framework tailored for Southeast Asia, addressing regional nuances in safety detection and outperforming existing models across multiple benchmarks.
Contribution
The paper presents a novel agentic data-generation framework and the SEA-Guard models, the first to incorporate SEA cultural contexts for multilingual safety in AI.
Findings
SEA-Guard outperforms existing safeguards in detecting regionally sensitive content.
The framework enables scalable creation of authentic, region-specific safety datasets.
SEA-Guard maintains strong general safety performance across benchmarks.
Abstract
Culturally aware safeguards are crucial for AI alignment in real-world settings, where safety extends beyond common sense and encompasses diverse local values, norms, and region-specific regulations. However, building large-scale, culturally grounded datasets is challenging due to limited resources and a scarcity of native annotators. Consequently, many safeguard models rely on machine translation of English datasets, often missing regional and cultural nuances. We present a novel agentic data-generation framework to scalably create authentic, region-specific safety datasets for Southeast Asia (SEA). On this foundation, we introduce the SEA-Guard family, the first multilingual safeguard models grounded in SEA cultural contexts. Evaluated across multiple benchmarks and cultural variants, SEA-Guard consistently outperforms existing safeguards at detecting regionally sensitive or harmful…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Topic Modeling · Occupational Health and Safety Research
