How attention works in neural networks: the core of LLMs and ChatGPT

The attention mechanism is what lets ChatGPT and other neural networks work out which words matter more. Habr has published a short breakdown of how this mechanism works, the one on which modern LLMs and transformers rest.

What is self-attention in neural networks?

Self-attention is a mechanism in which the model turns the input sequence into three representations (Query, Key, Value) and compares them to dynamically decide which parts of the input matter most to process for the current task.

How is attention used in LLMs?

In modern LLMs, attention lets the model choose which tokens to trust more when processing text, which gives a better grasp of context and of the relationships between words.

What is attention in neural networks?

It is a mechanism that lets a model dynamically decide which tokens matter more for the current task, using Query, Key and Value representations.

Which types of attention does the guide cover?

Self-attention, cross-attention and multi-head attention, with PyTorch examples.

Where is the attention mechanism used?

In transformers and modern LLMs. It is the basic idea behind ChatGPT, Claude and other language models.

Why is attention the foundation of ChatGPT and LLMs?

The attention mechanism lets transformers process sequences of words by selecting the important parts of the text. It underpins modern LLMs, including ChatGPT, allowing them to understand context and generate relevant answers.