Author(s): ml point
Originally published on Towards AI.
Measuring what AI actually understands about Urdu
As large language models increasingly promote themselves as multilingual, an important question often remains unanswered: how do we verify that claim for languages ​​outside the English-centric core? Despite being spoken by millions of people and possessing a deep literary and cultural tradition, Urdu has historically been evaluated through borrowed or translated criteria. UrduBench has emerged as an improvement on this practice.
UrduBench is a benchmarking framework specifically for Urdu, which aims to accurately assess how well NLP systems handle the language by focusing on original datasets rather than translated datasets. This highlights the importance of appropriate evaluation methods and highlights the limitations of multilingual models when handling the unique linguistic characteristics of Urdu, thereby contributing to more inclusive and accurate AI evaluation.
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Published via Towards AI
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Comment: The content represents the views of the contributing authors and not those of AI.
