@inproceedings{tasawong-etal-2026-sea,
title = "{SEA}-Guard: Culturally Grounded Multilingual Safeguard for {S}outheast {A}sia",
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.141/",
pages = "2917--2941",
ISBN = "979-8-89176-395-1",
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 content while maintaining strong general safety performance."
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%0 Conference Proceedings
%T SEA-Guard: Culturally Grounded Multilingual Safeguard for Southeast Asia
%A Tasawong, Panuthep
%A Ngui, Jian Gang
%A Aji, Alham Fikri
%A Cohn, Trevor
%A Limkonchotiwat, Peerat
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F tasawong-etal-2026-sea
%X 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 content while maintaining strong general safety performance.
%U https://aclanthology.org/2026.findings-acl.141/
%P 2917-2941
Markdown (Informal)
[SEA-Guard: Culturally Grounded Multilingual Safeguard for Southeast Asia](https://aclanthology.org/2026.findings-acl.141/) (Tasawong et al., Findings 2026)
ACL