Author(s): ML Point

Originally published on Towards AI.

Measuring What AI Actually Understands About Urdu

As large language models increasingly market themselves as multilingual, one critical question often remains unanswered: how do we verify that claim for languages outside the English-centric core? Urdu, despite being spoken by tens of millions and carrying a deep literary and cultural tradition, has historically been evaluated through borrowed or translated benchmarks. UrduBench emerges as a corrective to this practice.

UrduBench is a benchmarking framework specifically for Urdu, aiming to accurately assess how well NLP systems handle the language by focusing on native datasets instead of translated ones. It highlights the importance of proper evaluation methodologies and exposes the limitations of multilingual models when handling the unique linguistic characteristics of Urdu, thereby contributing to more inclusive and accurate AI evaluations.

Read the full blog for free on Medium.

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Published via Towards AI