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

• URL: http://arxiv.org/abs/2510.20043v1
• Date: Wed, 22 Oct 2025 21:42:59 GMT
• Title: From Facts to Folklore: Evaluating Large Language Models on Bengali Cultural Knowledge
• Authors: Nafis Chowdhury, Moinul Haque, Anika Ahmed, Nazia Tasnim, Md. Istiak Hossain Shihab, Sajjadur Rahman, Farig Sadeque,
• Abstract summary: We show that large language models (LLMs) struggle with cultural knowledge and performance when context is provided.<n>Our work addresses these limitations through a Bengali Language Cultural Knowledge dataset including folk traditions, culinary arts, and regional dialects.<n>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 when context is provided.
• Score: 7.322034156204158
• License: http://creativecommons.org/licenses/by/4.0/
• 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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