{"slug": "multilingual-in-name-only-cultural-and-linguistic-weaknesses-of-llms-in-urdu", "title": "Multilingual in Name Only? Cultural and Linguistic Weaknesses of LLMs in Urdu", "summary": "A study of 93 Urdu stories generated by three contemporary LLMs — GPT-5.1, Qwen-3-Max, and DeepSeek-3.1 — found that the models frequently make basic grammar and semantic errors, produce incoherent and repetitively unnatural text, and show pervasive cultural shallowness, according to the arXiv paper 2609.10758v1. The researchers manually annotated the generated corpus under a nine-label linguistic, semantic, and cultural taxonomy and reported that few-shot prompting left the cultural and context errors largely unresolved. The findings, which use Urdu as a representative low-resource language, highlight the limitations of current LLMs as a reliable source of content generation and information retrieval for low-resource languages.", "body_md": "arXiv:2609.10758v1 Announce Type: new \nAbstract: 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 generation of multilingual LLMs when used for the task of story generation. We consider Urdu language as a representative low-resource language. We generate Urdu-Stories, a corpus of 93 stories generated using three contemporary LLMs (GPT-5.1, Qwen-3-Max, DeepSeek-3.1). We manually annotate the errors present in them under a nine-label linguistic, semantic, and cultural taxonomy. Our notable findings suggest that LLMs often make basic errors of grammar and semantics. The stories lack coherence, have unnatural repetition and show pervasive cultural shallowness. We further show using few-shot prompting that the cultural and context errors largely remain unresolved. Our findings highlight the limitations of current LLMs as a reliable source of content generation and information retrieval for low-resource languages.", "url": "https://wpnews.pro/news/multilingual-in-name-only-cultural-and-linguistic-weaknesses-of-llms-in-urdu", "canonical_source": "https://arxiv.org/abs/2609.10758", "published_at": "2026-09-11 04:00:00+00:00", "updated_at": "2026-09-11 04:25:56.447130+00:00", "lang": "en", "topics": ["large-language-models", "natural-language-processing", "ai-research", "generative-ai"], "entities": ["GPT-5.1", "Qwen-3-Max", "DeepSeek-3.1", "Urdu-Stories", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/multilingual-in-name-only-cultural-and-linguistic-weaknesses-of-llms-in-urdu", "markdown": "https://wpnews.pro/news/multilingual-in-name-only-cultural-and-linguistic-weaknesses-of-llms-in-urdu.md", "text": "https://wpnews.pro/news/multilingual-in-name-only-cultural-and-linguistic-weaknesses-of-llms-in-urdu.txt", "jsonld": "https://wpnews.pro/news/multilingual-in-name-only-cultural-and-linguistic-weaknesses-of-llms-in-urdu.jsonld"}}