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arXiv Machine Learning By Farah Adeeba, Abdul Rafae Khan, Rajesh Bhatt, Hassan Sajjad

Multilingual in Name Only? Cultural and Linguistic Weaknesses of LLMs in Urdu

Read the original on arXiv Machine Learning →

The paper investigates how multilingual large language models perform when generating stories in Urdu, a low‑resource language. The authors created a corpus of 93 Urdu stories produced by GPT‑5.1, Qwen‑3‑Max, and DeepSeek‑3.1, and manually annotated errors across a nine‑label taxonomy covering linguistic, semantic, and cultural aspects. Findings reveal frequent grammatical and semantic mistakes, lack of coherence, unnatural repetition, and pervasive cultural shallowness, with few‑shot prompting failing to resolve many of these issues.

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