On the Scaling Laws of Geographical Representation in Language Models by Nathan Godey and colleagues presents an intriguing study that dives into how language models, including the behemoths known as Large Language Models (LLMs), incorporate and represent geographical information as they scale up.

Key Findings:

• Geographical knowledge is captured even by smaller language models.
• As the model size increases, this geographical representation scales in a consistent manner.
• Larger language models do not automatically correct the geographical biases present in their training datasets, an important consideration for developers and users of these technologies.

This paper propels the conversation forward on how language models could potentially perpetuate biases and highlights the importance of conscious data curation. It also sparks questions around what other types of biases may be scaling with the size of LLMs, urging the AI community to prioritize fairness and diversity in model training. Read the full article.