Large language models (LLMs) work by using deep neural networks to learn patterns and relationships in language data. The basic idea behind LLMs is that they can use vast amounts of text data to learn how words and phrases are used in context, and then use this understanding to generate new text or understand existing text.

The architecture of LLMs typically consists of several layers of neurons, with each layer learning progressively more complex features of language data. The initial layers of the network learn simple features like individual letters or words, while later layers learn more complex features like syntax and meaning. During training, the LLM is fed large amounts of text data, and its neural network adjusts its weights and biases to learn the statistical patterns in the data. This process is known as backpropagation, where the model's errors are propagated back through the network to adjust its parameters.

Once trained, LLMs can generate new text by sampling from their learned language patterns. They can also use their understanding of language patterns to perform a wide range of NLP tasks, including text classification, translation, and summarization.

One of the key innovations in LLMs is the use of self-attention mechanisms, which allow the model to focus on different parts of the input text when generating output. This has been particularly successful in models like GPT-3, which can generate high-quality text in a wide range of styles and genres.