UrduMMLU: A Massive Multitask Benchmark for Urdu Language Understanding
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 26,389 Urdu MCQs across 26 subjects and five domains, collected from native Urdu MCQ banks and public examination PDFs. Unlike translation-based benchmarks, UrduMMLU combines academic subjects with content specific to Urdu and regional education. We label the exam-derived portion through dual human annotation with strict consensus filtering. We evaluate 30 LLMs under English and Urdu prompts, yielding 60 zero-shot evaluations, and furt...
| Authors | Ahmer Tabassum, Sarfraz Ahmad, Hasan Iqbal et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 June 2026 |
| Last updated at source | 11 September 2026 |
Models and datasets referencing this paper
- MBZUAI/UrduMMLUDataset
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