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#}

大型语言模型（LLMs）要在特定国家有效部署，必须具备对该国文化和基本知识的理解。为此，我们引入了国家对齐度的概念，它从社会价值对齐和共同知识对齐两个方面度量了LLM与目标国家之间的一致性。我们构建了KorNAT，这是第一个用于衡量LLM与韩国的国家对齐度的基准。通过社会价值数据集和基本知识数据集对LLM进行评估，并且实验结果表明只有少数模型达到了我们的参考分数，具有进一步提升的潜力。KorNAT经过政府机构评估后获得了政府批准，我们的数据集的样本和评估协议可以在https://selectstar.ai/ko/papers-national-alignment#找到。

韩国社会价值和常识的LLM对齐基准(KorNAT)