New research tracks knowledge acquisition in language models during pretraining

A new research paper explores how language models acquire knowledge during pretraining by analyzing models trained on web snapshots from different years. Researchers developed a temporal variability score (TVS) to measure how knowledge prevalence changes over time in web text. Their findings indicate that items with higher temporal variability are learned more effectively later in the pretraining process and lead to better performance on knowledge-intensive tasks. AI

IMPACT Provides a new diagnostic for understanding how language models learn and represent knowledge over time.

RANK_REASON Research paper published on arXiv detailing a new method for analyzing model pretraining. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Hugging Face
• Point-in-Time Models
• TVS

• paper
• model release

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