Geography According to ChatGPT -- How Generative AI Represents and Reasons about Geography
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Abstract (arXiv)
Understanding how AI will represent and reason about geography should be a key concern for all of us, as the broader public increasingly interacts with spaces and places through these systems. Similarly, in line with the nature of foundation models, our own research often relies on pre-trained models. Hence, understanding what world AI systems construct is as important as evaluating their accuracy, including factual recall. To motivate the need for such studies, we provide three illustrative vignettes, i.e., exploratory probes, in the hope that they will spark lively discussions and follow-up work: (1) Do models form strong defaults, and how brittle are model outputs to minute syntactic variations? (2) Can distributional shifts resurface from the composition of individually benign tasks, e.g., when using AI systems to create personas? (3) Do we overlook deeper questions of understanding when solely focusing on the ability of systems to recall facts such as geographic principles?
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References · 15
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- 2023Whose Opinions Do Language Models Reflect?cited by 773
- 2020Knowledge Graphscited by 621
- 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agentscited by 392
- 2024A Roadmap to Pluralistic Alignmentcited by 184
- 2024One fish, two fish, but not the whole sea: Alignment reduces language models' conceptual diversitycited by 58
- 2020LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protectioncited by 57
- 2024TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learningcited by 31
- 2025Foundation Models for Geospatial Reasoning: Assessing Capabilities of Large Language Models in Understanding Geometries and Topological Spatial Relationscited by 23
- 2023Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detectioncited by 14
- 2025Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questionscited by 13
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