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

Domain:

natural language processing

Record type:

paperdataset
Creator:
ChoHaqAhmTas
Host:avatar
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. 4 pages

Visit

arxiv.org

Tags

Computation and LanguageMachine LearningI.2.7

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