From National Curricula to Cultural Awareness: Constructing Open-Ended Culture-Specific Question Answering Dataset

Abstract:Large language models (LLMs) achieve strong performance on many tasks, but their progress remains uneven across languages and cultures, often reflecting values latent in English-centric training data. To enable practical cultural alignment, we propose a scalable approach that leverages national social studies curricula as a foundation for culture-aware supervision. We introduce CuCu, an automated multi-agent LLM framework that transforms national textbook curricula into open-ended, culture-specific question-answer pairs for supervised fine-tuning (SFT). Applying CuCu to the Korean national social studies curriculum, we construct KCaQA, comprising 34.1k open-ended QA pairs. Our analyses and training experiments suggest that KCaQA covers culture-specific topics and produces responses grounded in local sociocultural contexts.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2601.04632 [cs.CL]

(or arXiv:2601.04632v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2601.04632 Focus to learn more arXiv-issued DOI via DataCite

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