SEA-SafeguardBench: Culturally Grounded Safety Benchmark for Southeast Asian Languages (2026.findings-acl)
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| Challenge: | Existing multilingual safety benchmarks rely on machine-translated English data, which fails to capture nuances in low-resource languages. |
| Approach: | They propose to use a human-verified safety benchmark for Southeast Asian languages to validate their safety and cultural diversity. |
| Outcome: | The proposed model outperforms existing models in general, in-the-wild, and content generation across eight languages and 21,640 samples across three subsets: general, and in- the-wild. |
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