Understanding Data Temporality Impact on Large Language Models Pre-training

Large language models' knowledge acquisition is significantly influenced by the temporality of their training data, with models trained on shuffled corpora lacking a clear understanding of temporal context. Researchers have investigated the impact of pre-training dynamics on the acquisition of time-sensitive factual knowledge, with a specific focus on the ordering of data¹. This study reveals that the order in which data is presented during pre-training affects the model's ability to learn and retain time-sensitive information. The findings suggest that models trained on chronologically ordered data perform better on tasks that require an understanding of temporal relationships. This has significant implications for the development of more accurate and reliable large language models. The impact of data temporality on model performance matters to practitioners, as it can affect the accuracy and reliability of AI systems in real-world applications.