Language models often perform far better in high-resource languages than in others, and that disparity is especially acute in Southeast Asia. A new arXiv paper introduces DeepSEA, a multilingual evaluation benchmark designed to measure model capabilities across the region's languages.
The benchmark directly targets the gap in testing resources for a region with around 700 million people. By providing a standard evaluation suite, DeepSEA aims to make it easier to compare models fairly and to expose where they still fall short.
Because this is a single source, the details are limited to what the abstract states. Still, the motivation is clear: without benchmarks like DeepSEA, progress in Southeast Asian languages remains hard to track and easy to overlook.