GeoLLM: Extracting Geospatial Knowledge from Large Language Models

arXiv:2310.06213v2 Announce Type: replace-cross
Abstract: The application of machine learning (ML) in a range of geospatial tasks is increasingly common but often relies on globally available covariates such as satellite imagery that can either be expensive or lack predictive power. Here we explore the question of whether the vast amounts of knowledge found in Internet language corpora, now compressed within large language models (LLMs), can be leveraged for geospatial prediction tasks. We first demonstrate that LLMs embed remarkable spatial information …

arxiv cs.cl cs.lg geospatial knowledge language language models large language large language models type