Understanding Data Temporality Impact on Large Language Models Pre-training

2026-05-21 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, quick

Summary

A study investigates the impact of data temporality, specifically ordering, on large language model (LLM) pre-training, addressing the issue of frozen and poorly understood temporal knowledge in models trained on shuffled corpora. Researchers introduced a benchmark of over 7,000 temporally grounded questions and an evaluation protocol to assess factual knowledge association with time periods. They pre-trained 6B-parameter models using temporally ordered Common Crawl snapshots and compared them against standard shuffled pre-training. Results indicate that sequentially trained models perform comparably to shuffled baselines on general language understanding and common knowledge, while consistently demonstrating more up-to-date and temporally precise knowledge. Temporally ordered pre-training enhances factual freshness, whereas shuffled pre-training tends to peak on older data, potentially due to increased factual repetition. This work provides a foundation for future continual learning research.

Key takeaway

For machine learning engineers developing large language models, consider integrating temporally ordered data into your pre-training pipelines. This approach can significantly improve your model's factual freshness and temporal precision without compromising general language understanding. You should explore the released benchmark and datasets to evaluate your models' temporal grounding and potentially adopt sequential pre-training to enhance knowledge currency, especially for applications requiring up-to-date information.

Key insights

Temporally ordering pre-training data significantly improves LLM factual freshness and temporal precision without sacrificing general knowledge.

Principles

• Sequential data ordering enhances factual freshness in LLMs.
• Shuffled pre-training may lead to factual repetition of older data.
• Temporal grounding can be evaluated with specific question benchmarks.

Method

Pre-train 6B-parameter LLMs on temporally ordered Common Crawl snapshots, then evaluate against shuffled baselines using a benchmark of over 7,000 temporally grounded questions.

In practice

• Utilize the released benchmark for temporal knowledge evaluation.
• Experiment with sequential data ordering for LLM pre-training.
• Explore continual learning approaches based on temporal data.

Topics

• Large Language Models
• Pre-training Data
• Data Temporality
• Factual Knowledge
• Continual Learning
• Common Crawl

Code references

• kyutai-labs/kairos

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.