
Developing large language models (LLMs) has traditionally involved the assumption that more pre-training data equates to better model performance. However, recent groundbreaking research introduces a cautionary note that has significant implications for the future of AI and language modeling. The phenomenon of “Catastrophic Overtraining,” revealed in recent studies, suggests that an excess of pre-training data may degrade the effectiveness of