Despite the impressive performance of Large Language Models (LLM) for various
natural language processing tasks, little is known about their comprehension of
geographic data and related ability to facilitate informed geospatial
decision-making. This paper investigates the extent of geospatial knowledge,
awareness, and reasoning abilities encoded within such pretrained LLMs. With a
focus on autoregressive language models, we devise experimental approaches
related to (i) probing LLMs for geo-coordinates to assess geospatial knowledge,
(ii) using geospatial and non-geospatial prepositions to gauge their geospatial
awareness, and (iii) utilizing a multidimensional scaling (MDS) experiment to
assess the models' geospatial reasoning capabilities and to determine locations
of cities based on prompting. Our results confirm that it does not only take
larger, but also more sophisticated LLMs to synthesize geospatial knowledge
from textual information. As such, this research contributes to understanding
the potential and limitations of LLMs in dealing with geospatial information.

该研究通过探测具备预训练能力的大型语言模型对地理数据的理解程度和与此相关的促进地理空间决策的能力，通过三个实验验证结论，表明合成地理空间知识需要更大规模和更复杂的语言模型，并且对于处理地理空间信息，大型语言模型的潜力和局限性值得研究。