Summary of Quantifying Geospatial in the Common Crawl Corpus, by Ilya Ilyankou et al.
Quantifying Geospatial in the Common Crawl Corpus
by Ilya Ilyankou, Meihui Wang, Stefano Cavazzi, James Haworth
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) are exhibiting emerging capabilities in geographic information systems (GIS), stemming from their pre-training on vast unlabelled text datasets. The Common Crawl (CC) corpus, a major source of these datasets, contains geospatial data that has been largely overlooked, affecting our understanding of LLMs’ spatial reasoning abilities. This paper investigates the prevalence of geospatial data in recent CC releases using Gemini 1.5, a powerful language model. By analyzing a sample of documents and manually revising the results, we estimate that approximately 18.7% of web documents in CC contain geospatial information such as coordinates and addresses. Our findings show little difference in prevalence between English- and non-English-language documents. This study provides quantitative insights into the nature and extent of geospatial data in CC, laying the groundwork for future studies on geospatial biases of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language models are getting better at understanding geographic information. It’s like a big puzzle, and these models are starting to figure out clues that help them understand where things are in the world. The researchers used a special model called Gemini 1.5 to look through a huge collection of web pages to see how often they contained important location details. They found that about one-fifth of all the pages had this kind of information, and it didn’t matter what language the page was in. This study helps us understand how these models are learning about geography and what we can learn from them. |
Keywords
» Artificial intelligence » Gemini » Language model » Stemming
