arXiv:2606.07167v1 Announce Type: cross Abstract: Meaningful multilingual evaluation must test models in the target language and educational context. Urdu, spoken by more than 230 million people, lacks a broad MMLU-style benchmark built from native educational sources. We introduce UrduMMLU, a benchmark of 26,431 Urdu MCQs across 26 subjects and five domains, collected from native Urdu MCQ banks and public examination PDFs. Unlike translation-based resources, UrduMMLU covers both standard academic subjects and Urdu- and region-specific content. We label the exam-derived portion through dual hu
The proliferation of AI models is driving the critical need for diverse, non-English language benchmarks to ensure equitable and culturally relevant AI development.
The creation of native language benchmarks like UrduMMLU is crucial for fostering sovereign AI capabilities, reducing dependency on Western-centric models, and enabling AI development relevant to local contexts.
The availability of UrduMMLU provides a standardized, locally relevant evaluation metric for AI models designed for Urdu speakers, moving beyond translation-based resources.
- · Pakistan
- · Urdu language speakers
- · AI developers focused on South Asia
- · Multilingual AI research
- · AI models without diverse language training
- · Monolingual AI development approaches
Improved performance and cultural relevance of AI models for Urdu speakers.
Increased investment in local AI R&D and data infrastructure within Urdu-speaking regions.
Reduced digital divide for Urdu speakers, potentially leading to new economic opportunities and educational advancements powered by culturally aware AI.
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Read at arXiv cs.AI