{"ID":2851945,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.20043","arxiv_id":"2510.20043","title":"From Facts to Folklore: Evaluating Large Language Models on Bengali Cultural Knowledge","abstract":"Recent progress in NLP research has demonstrated remarkable capabilities of large language models (LLMs) across a wide range of tasks. While recent multilingual benchmarks have advanced cultural evaluation for LLMs, critical gaps remain in capturing the nuances of low-resource cultures. Our work addresses these limitations through a Bengali Language Cultural Knowledge (BLanCK) dataset including folk traditions, culinary arts, and regional dialects. Our investigation of several multilingual language models shows that while these models perform well in non-cultural categories, they struggle significantly with cultural knowledge and performance improves substantially across all models when context is provided, emphasizing context-aware architectures and culturally curated training data.","short_abstract":"Recent progress in NLP research has demonstrated remarkable capabilities of large language models (LLMs) across a wide range of tasks. While recent multilingual benchmarks have advanced cultural evaluation for LLMs, critical gaps remain in capturing the nuances of low-resource cultures. Our work addresses these limitat...","url_abs":"https://arxiv.org/abs/2510.20043","url_pdf":"https://arxiv.org/pdf/2510.20043v1","authors":"[\"Nafis Chowdhury\",\"Moinul Haque\",\"Anika Ahmed\",\"Nazia Tasnim\",\"Md. Istiak Hossain Shihab\",\"Sajjadur Rahman\",\"Farig Sadeque\"]","published":"2025-10-22T21:42:59Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.LG\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}