This research focuses on assessing the ability of large language models (LLMs) in representing geometries and their spatial relations. We utilize LLMs including GPT-2 and BERT to encode the well-known text (WKT) format of geometries and then feed their embeddings into classifiers and regressors to evaluate the effectiveness of the LLMs-generated embeddings for geometric attributes. The experiments demonstrate that while the LLMs-generated embeddings can preserve geometry types and capture some spatial relations (up to 73% accuracy), challenges remain in estimating numeric values and retrieving spatially related objects. This research highlights the need for improvement in terms of capturing the nuances and complexities of the underlying geospatial data and integrating domain knowledge to support various GeoAI applications using foundation models.

研究使用大型语言模型（LLMs）对几何形状及其空间关系进行表示的能力，并使用GPT-2和BERT等LLMs对几何形状的文本（WKT）格式进行编码，然后将其嵌入分类器和回归器中评估LLMs生成的嵌入表示对几何属性的效果。实验证明，尽管LLMs生成的嵌入可以保留几何类型并捕捉一些空间关系（准确率高达73%），但在估计数值和检索空间相关对象方面仍存在挑战。此研究强调了在捕捉底层地理空间数据的细微差别和复杂性以及整合领域知识以支持各种基于GeoAI应用的需要改进的重要性。

评估大型语言模型在几何和空间关系的文本描述中的有效性