World Properties without World Models: Recovering Spatial and Temporal Structure from Co-occurrence Statistics in Static Word Embeddings

Research shows that static word embeddings like GloVe and Word2Vec can recover substantial geographic and temporal information from text co-occurrence patterns alone, challenging assumptions that such capabilities require sophisticated world models in large language models. The study found these simple embeddings could predict city coordinates and historical birth years with high accuracy, suggesting that linear probe recoverability doesn't necessarily indicate advanced internal representations.