For Large Language Models (LLMs) to be effectively deployed in a specific country, they must possess an understanding of the nation's culture and basic knowledge. To this end, we introduce National Alignment, which measures an alignment between an LLM and a targeted country from two aspects: social value alignment and common knowledge alignment. Social value alignment evaluates how well the model understands nation-specific social values, while common knowledge alignment examines how well the model captures basic knowledge related to the nation. We constructed KorNAT, the first benchmark that measures national alignment with South Korea. For the social value dataset, we obtained ground truth labels from a large-scale survey involving 6,174 unique Korean participants. For the common knowledge dataset, we constructed samples based on Korean textbooks and GED reference materials. KorNAT contains 4K and 6K multiple-choice questions for social value and common knowledge, respectively. Our dataset creation process is meticulously designed and based on statistical sampling theory and was refined through multiple rounds of human review. The experiment results of seven LLMs reveal that only a few models met our reference score, indicating a potential for further enhancement. KorNAT has received government approval after passing an assessment conducted by a government-affiliated organization dedicated to evaluating dataset quality. Samples and detailed evaluation protocols of our dataset can be found in \url{https://selectstar.ai/ko/papers-national-alignment#}

翻译：为使大型语言模型在特定国家有效部署，其必须理解该国文化与基础知识。为此，我们提出“国家对齐”概念，从社会价值观对齐与常识知识对齐两个维度衡量LLM与目标国家的一致性。社会价值观对齐评估模型理解国家特定社会价值观的能力，而常识知识对齐则检验模型掌握与该国相关基础知识的水平。我们构建了首个面向韩国的国家对齐基准——KorNAT。在社会价值观数据集方面，我们通过一项涉及6,174名韩国参与者的全国性大规模调查获取真实标签；在常识知识数据集方面，基于韩国教科书及普通教育发展（GED）参考资料构建样本。KorNAT分别包含4,000道与6,000道社会价值观与常识知识选择题。数据集构建流程基于统计抽样理论精心设计，并经多轮人工审核优化。对7个LLM的实验结果显示，仅少数模型达到参考分数，表明模型性能仍有提升空间。KorNAT已通过政府附属质量评估机构的数据集审核，获得官方批准。数据集的样本与详细评估方案可访问\url{https://selectstar.ai/ko/papers-national-alignment#}获取。