Pretraining Language Models on Historical Text

📰 ArXiv cs.AI

Learn how to pretrain language models on historical text to improve performance on related tasks and datasets

advanced Published 3 Jun 2026

Action Steps

Collect historical text data from diverse archival sources
Preprocess the data to prevent temporal leakage and ensure temporal consistency
Train a language model exclusively on the historical text data
Evaluate the model using reliable and temporally consistent metrics
Fine-tune the model for specific downstream tasks on historical text

Who Needs to Know This

NLP researchers and engineers can benefit from this knowledge to develop more accurate and informative language models for historical text analysis

Key Insight

💡 Pretraining language models on historical text can improve performance on related tasks and datasets, but requires careful consideration of data quality, temporal leakage, and evaluation metrics

Key Takeaways

Learn how to pretrain language models on historical text to improve performance on related tasks and datasets

Full Article

Title: Pretraining Language Models on Historical Text

Abstract:
arXiv:2606.02991v1 Announce Type: cross Abstract: We introduce TypewriterLM, a 7.24B History language model (LM) trained exclusively on English text predating 1913. Developing History LMs requires addressing challenges in data quality and availability, preventing temporal leakage, designing temporally consistent post-training pipelines, and constructing reliable evaluations. To address these issues, we construct TypewriterCorpus, a 54B-token historical corpus collected from diverse archival and

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