AI Singapore SEA-HELM v2 finds frontier LLMs perform 20–45% below English benchmarks on SEA professional tasks across 11 languages. Thai, Vietnamese, Bahasa, and Tagalog workflows need language validation — English accuracy benchmarks do not transfer to SEA deployments.
AI Singapore has released SEA-HELM v2 (Southeast Asian Holistic Evaluation of Language Models), a comprehensive benchmark evaluating LLM performance across 11 Southeast Asian languages on professional enterprise tasks. The benchmark tests Thai, Vietnamese, Bahasa Indonesian, Bahasa Malaysian, Filipino/Tagalog, Burmese, Khmer, Lao, Sinhalese, Tamil (Singapore and Malaysia), and English — providing the most comprehensive multilingual performance data for APAC enterprise AI practitioners.
Key findings from SEA-HELM v2: frontier English-primary models (GPT-4, Claude, Gemini) perform 20–45% below their English benchmark on professional task accuracy in Southeast Asian languages — with larger gaps in low-resource languages (Khmer, Lao, Burmese) and smaller gaps in Indonesian and Vietnamese, which are better represented in training data. The research identifies specific task types with largest gaps: legal document interpretation, regulatory text comprehension, and culturally contextualised customer communication. For APAC enterprise AI practitioners deploying LLMs for customer-facing or professional workflows in Southeast Asian markets, SEA-HELM v2 provides empirical evidence that English performance benchmarks do not transfer to SEA language contexts — and that language-specific validation is mandatory before production deployment.
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