Current AI language model benchmarks significantly underrepresent the world's linguistic diversity, with the broadest benchmarks covering only about 2.9% of the roughly 7,000 living languages. Even the most comprehensive text benchmark, FLORES-200, includes only 200 languages, while widely cited benchmarks like MMLU focus on a single language, English. This limited coverage means that while models may demonstrate translation capabilities for a few dozen languages, their performance on complex tasks like reasoning, safety, or instruction following is rarely evaluated in the vast majority of the world's languages. AI
IMPACT Highlights a critical gap in AI development, suggesting that current models' capabilities are poorly understood for most of the world's linguistic communities.
RANK_REASON Analysis of existing benchmarks and their coverage of world languages. [lever_c_demoted from research: ic=1 ai=1.0]
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