The global use of artificial intelligence has increased interest in assessing the ability to generate culturally localized content, including stories. Cultural localization in stories often occurs through either templated localization -- the use of cultural markers (e.g., names, locations) in a generic narrative -- or holistic localization -- the variation of plots, values, and themes, in addition to cultural markers. We propose a method to measure the degree to which content was generated through templated localization. Specifically, we identify the lexical tokens that distinguish stories across nationalities and measure the similarity of the narratives that remain after removing them. In stories generated by five models on 125 topics for 193 nationalities, our method is able to detect that only a small subset (9-17%) of the vocabulary accounts for the variation across nationalities and that the narratives that remain after removing them contain repeated multi-word sequences, suggesting the presence of a shared culturally-agnostic narrative template. Finally, we characterize the cultural markers for their stereotypicality and offensiveness, finding that markers from 19 countries, mostly located in the Global South, are on average offensive.

翻译：人工智能的全球应用日益引发对其生成文化本地化内容能力的关注，尤其是故事创作方面。故事中的文化本地化通常通过两种方式实现：模板化本地化——在通用叙事中使用文化标记（如姓名、地点）；或整体本地化——除文化标记外，对情节、价值观和主题进行变化。我们提出了一种衡量内容通过模板化本地化生成程度的方法。具体而言，我们识别区分不同国家故事的语言标记，并测量移除这些标记后剩余叙事的相似性。在基于125个主题、193个国家的五个模型生成的故事中，我们的方法能够检测到仅有一小部分词汇（9-17%）解释了国家间的差异，而移除这些词汇后剩余的叙事中反复出现多词序列，暗示存在一个共享的、与文化无关的叙事模板。最后，我们刻画了文化标记的刻板性和冒犯性程度，发现来自19个国家（主要位于全球南方）的标记平均具有冒犯性。