While a large body of work inspects language models for biases concerning gender, race, occupation and religion, biases of geographical nature are relatively less explored. Some recent studies benchmark the degree to which large language models encode geospatial knowledge. However, the impact of the encoded geographical knowledge (or lack thereof) on real-world applications has not been documented. In this work, we examine large language models for two common scenarios that require geographical knowledge: (a) travel recommendations and (b) geo-anchored story generation. Specifically, we study four popular language models, and across about $100$K travel requests, and $200$K story generations, we observe that travel recommendations corresponding to poorer countries are less unique with fewer location references, and stories from these regions more often convey emotions of hardship and sadness compared to those from wealthier nations.

翻译：尽管已有大量研究考察语言模型在性别、种族、职业和宗教方面的偏见，但地理性质的偏见相对较少被探讨。一些近期研究对大型语言模型编码地理空间知识的程度进行了基准测试。然而，已编码的地理知识（或其缺失）对实际应用的影响尚未得到记录。在本研究中，我们针对两种需要地理知识的常见场景检验了大型语言模型：（a）旅行推荐和（b）地理锚定的故事生成。具体而言，我们研究了四种流行的语言模型，通过约$100$K条旅行请求和$200$K个故事生成，我们观察到针对较贫穷国家的旅行推荐独特性较低且地点引用较少，而与较富裕国家相比，来自这些地区的故事更频繁地传达艰辛和悲伤的情感。