Understanding Data Temporality Impact on Large Language Models Pre-training

2026-05-21

AI Summary

This work investigates how the temporal ordering of training data affects large language models’ ability to acquire time-sensitive knowledge. Training a 6B-parameter model on chronologically ordered Common Crawl snapshots, the study systematically examines whether sequential exposure to temporally structured corpora mitigates knowledge freezing—a common issue in models trained on shuffled data that impairs their capacity to accurately associate facts with their occurrence times. The authors introduce a novel evaluation benchmark comprising over 7,000 temporally grounded questions and provide the first empirical evidence that temporally ordered pretraining significantly enhances model performance on fact freshness and temporal accuracy, without compromising general language understanding capabilities compared to conventional shuffled-data training.

Abstract

Large language models (LLMs) are typically trained on shuffled corpora, yielding models whose knowledge is frozen at train time and whose temporal grounding remains poorly understood. In this work, we study the impact of pre-training dynamics on the acquisition of time-sensitive factual knowledge, focusing specifically on data ordering. Our main contributions are twofold. First, we introduce a comprehensive benchmark of over 7,000 temporally grounded questions and an evaluation protocol that enables analysis of whether models correctly associate facts with their corresponding time periods. Second, we pretrain 6B-parameter models on temporally ordered Common Crawl snapshots and compare them against standard shuffled pre-training. Our results show that sequentially trained models match shuffled baselines on general language understanding and common knowledge while consistently exhibiting more up-to-date and temporally precise knowledge. Temporally ordered pre-training yields improved factual freshness, while shuffled pre-training peaks on older data, possibly due to increased factual repetition. These findings, along with the release of our code at https://github.com/kyutai-labs/kairos , checkpoints, and datasets at https://huggingface.co/collections/kyutai/kairos provide a foundation for future research on continual learning for LLMs.

Problem

• data temporality
• large language models
• temporal grounding
• pre-training
• factual knowledge

Innovation

• temporal grounding
• data temporality
• time-sensitive knowledge
• ordered pre-training
• continual learning