Summary of On the Scaling Laws Of Geographical Representation in Language Models, by Nathan Godey et al.
On the Scaling Laws of Geographical Representation in Language Models
by Nathan Godey, Éric de la Clergerie, Benoît Sagot
First submitted to arxiv on: 29 Feb 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 The paper proposes to bridge the gap between established and recent literature on language models by investigating how geographical knowledge evolves when scaling language models. The research shows that geographical information is embedded in hidden representations of even tiny models, and scales consistently as model size increases. However, larger models cannot mitigate the geographical bias inherent to training data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how language models learn about geography. It finds that small and large language models both know where places are, but bigger models don’t make this knowledge any better or worse. The research shows that language models pick up on where places are from their training data, which can be biased towards certain regions. |
