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

Source: [From National Curricula to Cultural Awareness: Constructing Open-Ended Culture-Specific Question Answering Dataset](<https://www.sota2.com/research/paper/69608b6553fdd5c361744150>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-10T05:00:05.200Z

Authors: Haneul Yoo, Won Ik Cho, Geunhye Kim, Jiyoon Han

Published: 2026-01-08T06:04:59.000Z

## 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. Applying CuCu to the Korean national social studies curriculum, we construct KCaQA, comprising 34.1k open-ended QA pairs. Our quantitative and qualitative analyses suggest that KCaQA covers culture-specific topics and produces responses grounded in local sociocultural contexts.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2601.04632>)

[PDF](<https://arxiv.org/pdf/2601.04632.pdf>)