Most current LLMs use transformer blocks and next-token or related objectives to learn statistical patterns in text and code. Prompting, retrieval, tools, or fine-tuning adapt those learned patterns to downstream tasks.
A large language model is a neural network trained on extensive language data to predict or generate token sequences.
Most current LLMs use transformer blocks and next-token or related objectives to learn statistical patterns in text and code. Prompting, retrieval, tools, or fine-tuning adapt those learned patterns to downstream tasks.