What is a large language model (LLM)?

An LLM is an AI program that can understand and produce text. It is trained on vast amounts of data and uses a neural network
called a transformer model.

Simply put, an LLM is a computer program that learns to understand and interpret human language by studying a massive amount of text, often collected from the internet. However, to ensure quality, programmers may use a carefully selected set of data.

LLMs use deep learning, a kind of machine learning, to understand how words and sentences work together. Deep learning analyzes large amounts of unstructured data, helping the model recognize patterns and differences in content without human help.

LLMs are further trained through fine-tuning or prompt-tuning to specialize in specific tasks, like answering questions or translating languages. This extra training helps them perform better in their intended roles.

LLM neural networks
LLMs use neural networks to enable deep learning. Similar to how the human brain has neurons that send signals to each other,
neural networks consist of nodes connected to one another. They have several layers: an input layer, an output layer,
and one or more hidden layers in between. Information is passed between these layers only when their outputs meet a certain threshold.

LLM transformer models
The neural networks used in LLMs are called transformer models. These models excel at learning context, which is crucial for understanding human language. They use a technique called self-attention, which helps them detect how elements in a sequence are related.
This allows transformer models to better understand how the beginning and end of a sentence connect, and how sentences
in a paragraph relate to each other, making them more effective at grasping context than other machine learning models.

This ability allows LLMs to interpret human language, even when it’s vague, poorly defined, or presented in new ways.
They “understand” semantics by recognizing how words and concepts are related, having encountered them grouped together millions
or even billions of times during training. This helps them make sense of unfamiliar combinations and contexts.

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