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How do large language models (LLMs) work?

Large Language Models (LLMs) are artificial intelligence systems designed to understand and generate natural language. They are based on deep neural networks, specifically an architecture called Transformer.

The way LLMs work can be roughly divided into three steps: Training, Coding, and Decoding.

Training: LLMs are trained with large amounts of text data, which can come from various sources such as books, articles, web pages, forums, etc. This text data serves as the training dataset. During training, the model learns the statistical relationships, patterns, and structures of the language.

Coding: once the LLM is trained, an input in the form of text is given to the model. The text is broken down into tokens, which are individual words or subwords. Each token is then converted into a numerical vector that serves as input to the neural network. The model processes these vectors in layers of neurons and computes complex mathematical operations to recognize patterns and meanings in the text.

Decoding: after the input is encoded, the model can generate a response or a continuation of the text. This step is called decoding. The model calculates the probabilities for different words or tokens that could come next, and selects the most likely token based on those probabilities. This process is repeated iteratively to generate the text incrementally.

The power of LLMs relies on the enormous size of the neural network and the amount of training data. By training on large data sets, LLMs can learn a wide range of knowledge about language and the world. They can answer questions, compose texts, perform translations, simulate dialogues, and much more.

It is important to note that LLMs base their answers solely on statistical relationships in the training data set. They have no actual understanding of meaning or context, and thus can sometimes generate incorrect or inappropriate responses.