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

arXiv:2601.04632 · cs.CL, cs.AI · Submitted 2026-01-08 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-01-08

Updated: 2026-08-26

Comments: EMNLP 2026

Code: https://github.com/haneul-yoo/cucu

License: http://creativecommons.org/licenses/by-sa/4.0/

The gist: 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.

Terminology

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.

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