Abstract

Language is a process that changes over time as new vocabulary emerges, word meanings shift, and narratives progress. Despite this fact, most Large Language Models are trained on corpora that lack explicit temporal information, which inhibits their ability to model the language process. In this work, we introduce the Temporal Language Model 1 (TLM-1), a BERT style transformer encoder that models that language process by jointly learning to predict document contents and classify document publication dates. We also introduce a Bayesian framework for querying TLM-1 that disentangles its temporal dynamics from several sources of anachronism. Using this query framework, we demonstrate that TLM-1 effectively surfaces several sociolinguistic trends in contemporary American English and accurately detects semantic changes in word meanings. Furthermore, we perform a mechanistic analysis of TLM-1’s time token embeddings, and find that they learn a curve whose geometry recovers the ordinal progression of time. We take the existence of this curve as evidence that TLM-1 is effectively learning to reconstruct temporal language dynamics.