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Introduces SEA-NLI, a culturally grounded NLI benchmark covering eight Southeast Asian countries, revealing low performance of LLMs on culturally specific knowledge, especially in languages and science/technology. Shows that culture-aware prompting helps but chain-of-thought offers limited gains.
SEA-Embedding presents a fully open and reproducible text embedding pipeline for Southeast Asian languages, trained solely on public data, achieving state-of-the-art results on the SEA-BED benchmark.
An article exploring the history and cultural significance of fish sauce, a fundamental condiment in Southeast Asian cuisine made from fermented fish and salt, tracing its origins from ancient times to modern-day usage across Vietnam, Thailand, and other regions.
This paper introduces Anthropogenic Regional Adaptation, a paradigm for optimizing vision-language models to specific regional contexts while maintaining global generalization. The authors propose GG-EZ, an adaptation method using regional data filtering and model merging, demonstrating 5-15% improvements in cultural relevance for Southeast Asia across three VL architectures.
Grab uses OpenAI's GPT-4o vision fine-tuning to improve GrabMaps, achieving significant accuracy gains in speed limit sign localization (13%), lane counting (20%), and reducing manual mapping efforts across Southeast Asia's complex road networks.