Large Language Models (LLMs) are poised to play an increasingly important role in our lives, providing assistance across a wide array of tasks. In the geospatial domain, LLMs have demonstrated the ability to answer generic questions, such as identifying a country's capital; nonetheless, their utility is hindered when it comes to answering fine-grained questions about specific places, such as grocery stores or restaurants, which constitute essential aspects of people's everyday lives. This is mainly because the places in our cities haven't been systematically fed into LLMs, so as to understand and memorize them. This study introduces a novel framework for fine-tuning a pre-trained model on city-specific data, to enable it to provide accurate recommendations, while minimizing hallucinations. We share our model, LAMP, and the data used to train it. We conduct experiments to analyze its ability to correctly retrieving spatial objects, and compare it to well-known open- and closed- source language models, such as GPT-4. Finally, we explore its emerging capabilities through a case study on day planning.

翻译：大语言模型有望在我们的生活中扮演越来越重要的角色，为广泛的任务提供帮助。在地理空间领域，大语言模型已展示出回答一般性问题的能力（如识别国家首都）。然而，在回答关于特定地点的细粒度问题时（如杂货店或餐馆——这些构成人们日常生活关键部分的地点），其效用受到限制。这主要是因为城市中的地点尚未被系统地输入大语言模型，使其能够理解并记忆这些信息。本研究提出一种新框架，通过在特定城市数据上微调预训练模型，使其能够提供准确推荐，同时最大程度减少幻觉。我们公开了我们的模型LAMP及其训练所使用的数据。我们通过实验分析了该模型正确检索空间对象的能力，并将其与著名开源和闭源语言模型（如GPT-4）进行对比。最后，我们通过一个日计划案例研究，探索了其涌现能力。