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LLMs struggle with Urdu story generation, research finds

By PulseAugur Editorial · [1 sources] ·

A new research paper has evaluated the capabilities of multilingual large language models (LLMs) in generating content for the Urdu language, a low-resource language. The study found that models like GPT-5.1, Qwen-3-Max, and DeepSeek-3.1 struggle with Urdu story generation, producing errors in grammar, semantics, coherence, and cultural relevance. Despite efforts with few-shot prompting, these limitations persist, indicating that current LLMs are not yet reliable for content creation or information retrieval in such languages. AI

IMPACT Highlights significant limitations of current LLMs for low-resource languages, impacting global accessibility and content generation.

RANK_REASON Academic paper evaluating LLM performance on a low-resource language. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle with Urdu story generation, research finds

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Academic paper evaluating LLM performance on a low-resource language. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Farah Adeeba, Abdul Rafae Khan, Rajesh Bhatt, Hassan Sajjad ·

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

    arXiv:2609.10758v1 Announce Type: new Abstract: Multilingual large language models (LLMs) are increasingly used for open-ended text generation, yet their behaviour in low-resource languages remains poorly understood. In this work, we question how correct and reliable is the gener…

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