Large language models (LLMs) have shown remarkable capabilities across a
broad range of tasks involving question answering and the generation of
coherent text and code. Comprehensively understanding the strengths and
weaknesses of LLMs is beneficial for safety, downstream applications and
improving performance. In this work, we investigate the degree to which GPT-4
has acquired factual geographic knowledge and is capable of using this
knowledge for interpretative reasoning, which is especially important for
applications that involve geographic data, such as geospatial analysis, supply
chain management, and disaster response. To this end, we design and conduct a
series of diverse experiments, starting from factual tasks such as location,
distance and elevation estimation to more complex questions such as generating
country outlines and travel networks, route finding under constraints and
supply chain analysis. We provide a broad characterisation of what GPT-4
(without plugins or Internet access) knows about the world, highlighting both
potentially surprising capabilities but also limitations.

本篇研究探究 GPT-4 模型应用于地理数据场景问题的解决能力，试图了解其是否具备对地理位置、距离、海拔，以及更复杂的问题如国家轮廓、旅游网络、路线规划和供应链分析的理解和解决能力。结果表明该模型在一定程度上具备对世界的认识，但仍存在限制。