Geography According to ChatGPT -- How Generative AI Represents and Reasons about Geography
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911530379902976 |
|---|---|
| author | Janowicz, Krzysztof Mai, Gengchen Zhu, Rui Gao, Song Wang, Zhangyu Hu, Yingjie Bennett, Lauren |
| author_facet | Janowicz, Krzysztof Mai, Gengchen Zhu, Rui Gao, Song Wang, Zhangyu Hu, Yingjie Bennett, Lauren |
| contents | 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? |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18881 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Geography According to ChatGPT -- How Generative AI Represents and Reasons about Geography Janowicz, Krzysztof Mai, Gengchen Zhu, Rui Gao, Song Wang, Zhangyu Hu, Yingjie Bennett, Lauren Artificial Intelligence Computers and Society 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? |
| title | Geography According to ChatGPT -- How Generative AI Represents and Reasons about Geography |
| topic | Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2603.18881 |