How do LLMs learn patterns during training?

LLMs learn patterns by predicting the next word in a sequence during their training phase. This process involves analyzing vast amounts of text data, identifying statistical relationships between words, and adjusting their internal parameters to minimize prediction errors. Through repeated predictions and feedback, the model develops a sophisticated understanding of grammar, syntax, semantics, and even contextual nuances. Scanning only popular websites would provide an insufficient and biased dataset. Memorizing documents word for word would lead to overfitting and an inability to generalize to new inputs. Following pre-programmed developer rules would limit the model's ability to learn and adapt autonomously from data, contradicting the core principle of machine learning.