Transformer Architecture

Executive Definition: Transformer Architecture — A neural network design that processes data sequences in parallel using self-attention, forming the foundation of modern large language models.

Transformers use a self-attention mechanism to weigh the contextual importance of each part of an input sequence relative to the rest. By enabling parallel processing rather than sequential word-by-word analysis, they train significantly faster and capture long-range dependencies better than older architectures, enabling the massive scale of today’s LLMs.

Frequently Asked Questions

Why did Transformers replace older architectures?

Transformers process entire sequences in parallel rather than word-by-word. This makes training significantly faster and allows models to capture long-range dependencies in text better than recurrent neural networks (RNNs).

Do all modern AI models use Transformers?

Nearly all modern language models do. While some vision and audio models use hybrid architectures, Transformers dominate natural language processing.

Who invented the Transformer?

Google researchers introduced it in the 2017 paper ‘Attention Is All You Need.’ It has since become the foundation for GPT, BERT, Claude, Llama, and virtually all major LLMs.