Quantifying Geospatial in the Common Crawl Corpus
<|reference_start|>Quantifying Geospatial in the Common Crawl Corpus: Large language models (LLMs) exhibit emerging geospatial capabilities, stemming from their pre-training on vast unlabelled text datasets that are often derived from the Common Crawl (CC) corpus. However, the geospatial content within CC remains largely unexplored, impacting our understanding of LLMs' spatial reasoning. This paper investigates the prevalence of geospatial data in recent Common Crawl releases using Gemini 1.5, a powerful language model. By analyzing a sample of documents and manually revising the results, we estimate that 18.7% of web documents in CC contain geospatial information such as coordinates and addresses. We find little difference in prevalence between Enlgish- and non-English-language documents. Our findings provide quantitative insights into the nature and extent of geospatial data in CC, and lay the groundwork for future studies of geospatial biases of LLMs.<|reference_end|>
@article{ilyankou2024quantifying,
    title={Quantifying Geospatial in the Common Crawl Corpus},
    author={Ilya Ilyankou, Meihui Wang, Stefano Cavazzi, James Haworth},
    journal={arXiv preprint arXiv:2406.04952},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04952},
    primaryClass={cs.CL cs.AI}
}