SEA-Guard (SEA-LION-Guard v1)
Collection
Renamed from SEA-Guard to SEA-LION-Guard v1 • 8 items • Updated • 1
Paper: https://aclanthology.org/2026.findings-acl.194/
@inproceedings{tasawong-etal-2026-sea-safeguardbench,
title = "{SEA}-{S}afeguard{B}ench: Culturally Grounded Safety Benchmark for {S}outheast {A}sian Languages",
author = "Tasawong, Panuthep and
Ngui, Jian Gang and
Aji, Alham Fikri and
Cohn, Trevor and
Limkonchotiwat, Peerat",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.194/",
doi = "10.18653/v1/2026.findings-acl.194",
pages = "3973--4003",
ISBN = "979-8-89176-395-1",
abstract = "Safeguard models help large language models (LLMs) detect and block harmful content, but most evaluations remain English-centric and overlook linguistic and cultural diversity. Existing multilingual safety benchmarks often rely on machine-translated English data, which fails to capture nuances in low-resource languages. Southeast Asian (SEA) languages are underrepresented despite the region{'}s linguistic diversity and unique safety concerns, from culturally sensitive political speech to region-specific misinformation. Addressing these gaps requires benchmarks that are natively authored to reflect local norms and harm scenarios. We introduce SEA-SafeguardBench, the first human-verified safety benchmark for SEA, covering eight languages, 21,640 samples, across three subsets: general, in-the-wild, and content generation. The experimental results from our benchmark demonstrate that even state-of-the-art LLMs and guardrails are challenged by SEA cultural and harm scenarios and underperform when compared to English texts."
}