Understanding Data Temporality Impact on Large Language Models Pre-training

Problem
This preprint addresses the gap in understanding how data temporality affects the pre-training of large language models (LLMs). Traditional LLMs are trained on shuffled corpora, which may hinder their ability to acquire and retain time-sensitive factual knowledge. The authors aim to elucidate the impact of data ordering on the temporal grounding of knowledge in LLMs, a topic that has not been thoroughly explored in existing literature.

Method
The authors introduce a novel benchmark comprising over 7,000 temporally grounded questions, designed to evaluate the ability of LLMs to associate facts with their corresponding time periods. They pretrain 6B-parameter models using temporally ordered snapshots from the Common Crawl dataset, contrasting this approach with standard shuffled pre-training. The evaluation protocol assesses models on their factual accuracy and temporal precision. The training compute details are not disclosed, but the methodology emphasizes the importance of data ordering in the pre-training phase.

Results
The results indicate that models pretrained on temporally ordered data perform comparably to those trained on shuffled data in terms of general language understanding and common knowledge tasks. However, the temporally ordered models demonstrate significantly improved factual freshness and temporal precision. Specifically, the sequentially trained models consistently outperform their shuffled counterparts on the benchmark, particularly in their ability to provide up-to-date information. The authors note that shuffled pre-training tends to peak on older data, likely due to increased factual repetition, which diminishes the model’s temporal accuracy.

Limitations
The authors acknowledge several limitations, including the potential biases in the Common Crawl dataset and the specific focus on factual knowledge without exploring other dimensions of language understanding. They also do not address the scalability of their approach to larger models or different datasets. Additionally, the impact of temporal ordering on other tasks beyond factual knowledge remains unexplored, which could limit the generalizability of their findings.

Why it matters
This work has significant implications for the development of LLMs, particularly in the context of continual learning and the integration of temporal knowledge. By demonstrating that data ordering can enhance the temporal grounding of knowledge, the authors provide a foundation for future research aimed at improving LLMs’ ability to retain and utilize time-sensitive information. This could lead to more accurate and contextually aware models, which are crucial for applications in dynamic environments where factual accuracy over time is essential.

Authors: Pilchen Hippolyte, Fabre Romain, Signe Talla Franck, Perez Patrick, Grave Edouard
Source: arXiv cs.AI
URL: https://arxiv.org/abs/2605.22769v1