@inproceedings{duderstadt-helm-2026-model,
title = "A Model of the Language Process",
author = "Duderstadt, Brandon and
Helm, Hayden",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.590/",
pages = "12948--12959",
ISBN = "979-8-89176-390-6",
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."
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%0 Conference Proceedings
%T A Model of the Language Process
%A Duderstadt, Brandon
%A Helm, Hayden
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F duderstadt-helm-2026-model
%X 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.
%U https://aclanthology.org/2026.acl-long.590/
%P 12948-12959
Markdown (Informal)
[A Model of the Language Process](https://aclanthology.org/2026.acl-long.590/) (Duderstadt & Helm, ACL 2026)
ACL
- Brandon Duderstadt and Hayden Helm. 2026. A Model of the Language Process. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12948–12959, San Diego, California, United States. Association for Computational Linguistics.