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This study demonstrates that pretraining on historical text can yield a temporally grounded language model capable of maintaining reasonable performance even with limited modern data. The authors address critical challenges in data quality and temporal leakage while releasing TypewriterLM, a 7.24B-parameter model restricted to vocabulary from documents prior to 1913. This work advances the field by providing new benchmarks and resources for researching history-specific large language models without relying on contemporary internet corpora.
A Language Model from 1913: Pretraining on Historical Text
arXiv:2606.02991v2 Announce Type: replace-cross Abstract: While modern language models increasingly rely on ever-larger web corpora, we show that pretraining on historical text (e.g., pre-1913 text) in a data-constrained setting can produce a temporally grounded language model that still shows reasonable performance on language understanding. However, developing History LMs requires addressing challenges in data quality, preventing temporal leakage in post-training, and constructing temporally aligned evaluations. We address these challenges and pretrain TypewriterLM, a 7.24B-parameter model with a 1913 knowledge cutoff. We construct TypewriterCorpus, a 54B-token historical corpus with extensive temporal filtering, propose lexically grounded instruction tuning that constrains all responses to vocabulary from historical source documents, and introduce History-Event, a benchmark of 2,344 events for evaluating both competence and cutoff adherence. We release TypewriterLM and all associated resources to support future research on History LMs.
Abstract:While modern language models increasingly rely on ever-larger web corpora, we show that pretraining on historical text (e.g., pre-1913 text) in a data-constrained setting can produce a temporally grounded language model that still shows reasonable performance on language understanding. However, developing History LMs requires addressing challenges in data quality, preventing temporal leakage in post-training, and constructing temporally aligned evaluations. We address these challenges and pretrain TypewriterLM, a 7.24B-parameter model with a 1913 knowledge cutoff. We construct TypewriterCorpus, a 54B-token historical corpus with extensive temporal filtering, propose lexically grounded instruction tuning that constrains all responses to vocabulary from historical source documents, and introduce History-Event, a benchmark of 2,344 events for evaluating both competence and cutoff adherence. We release TypewriterLM and all associated resources to support future research on History LMs.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2606.02991 [cs.CL]
(or arXiv:2606.02991v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.02991 Focus to learn more arXiv-issued DOI via DataCite
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