Large Language Model

Large Language Models are neural networks trained on billions to trillions of tokens of text. Through this training, they develop emergent capabilities including reasoning, coding, summarization, and translation — all from a single model.

Modern LLMs use the Transformer architecture and are trained using unsupervised pre-training (predict the next token) plus RLHF (reinforcement learning from human feedback) to align output with human preferences.