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arXiv · 2510.20043

From Facts to Folklore: Evaluating Large Language Models on Bengali Cultural Knowledge

Nafis Chowdhury·Moinul Haque·Anika Ahmed·Nazia Tasnim·Md. Istiak Hossain Shihab·Sajjadur Rahman·Farig Sadeque
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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.

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Nafis Chowdhury, Moinul Haque, Anika Ahmed, Nazia Tasnim, Md. Istiak Hossain Shihab, Sajjadur Rahman, Farig Sadeque. 2025-10-22. From Facts to Folklore: Evaluating Large Language Models on Bengali Cultural Knowledge. https://arxiv.org/abs/2510.20043

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