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。

GPT4GEO：语言模型如何看待世界地理

GPT4GEO: How a Language Model Sees the World's Geography

Text-based delivery addresses, as the data foundation for logistics systems,
contain abundant and crucial location information. How to effectively encode
the delivery address is a core task to boost the performance of downstream
tasks in the logistics system. Pre-trained Models (PTMs) designed for Natural
Language Process (NLP) have emerged as the dominant tools for encoding semantic
information in text. Though promising, those NLP-based PTMs fall short of
encoding geographic knowledge in the delivery address, which considerably trims
down the performance of delivery-related tasks in logistic systems such as
Cainiao. To tackle the above problem, we propose a domain-specific pre-trained
model, named G2PTL, a Geography-Graph Pre-trained model for delivery address in
Logistics field. G2PTL combines the semantic learning capabilities of text
pre-training with the geographical-relationship encoding abilities of graph
modeling. Specifically, we first utilize real-world logistics delivery data to
construct a large-scale heterogeneous graph of delivery addresses, which
contains abundant geographic knowledge and delivery information. Then, G2PTL is
pre-trained with subgraphs sampled from the heterogeneous graph. Comprehensive
experiments are conducted to demonstrate the effectiveness of G2PTL through
four downstream tasks in logistics systems on real-world datasets. G2PTL has
been deployed in production in Cainiao's logistics system, which significantly
improves the performance of delivery-related tasks.

提出了一种新型的基于地理关系的预训练模型 G2PTL，它采用了真实的物流交付数据构建了一个包含丰富地理知识和交付信息的大规模异构图，并通过采样子图进行预训练，通过真实世界数据的四项下游任务的全面实验表明，G2PTL 能够显著提高物流系统中与交付相关任务的性能。