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.

通过在特定城市数据上对预训练模型进行微调，本研究引入了一种新的框架，从而使其能够提供准确的推荐，同时最大限度地减少虚构。我们分享了我们的模型LAMP和用于训练它的数据，通过实验分析其正确检索空间对象的能力，并将其与GPT-4等众所周知的开源和封闭源语言模型进行比较。最后，我们通过一次日程规划的案例研究探索它的新兴能力。

LAMP：一种地图上的语言模型