Transformer Architecture Explained

What Is the Transformer Architecture

The transformer architecture is a neural design that models token relationships across whole sequences. It can process text in parallel and learn high-quality contextual representations.

Attention Mechanisms

Attention mechanisms assign dynamic weights to relevant tokens when producing each output state. This improves handling of long-range dependencies and lexical ambiguity.

Self-Attention and Context Representation

Self-attention compares every token with every other token in the same sequence. The resulting context-aware vectors help models represent syntax, semantics, and discourse in a unified way.

Encoder–Decoder Structure

Classic transformer models use an encoder to represent source text and a decoder to generate target text. This structure remains central in sequence-to-sequence tasks such as translation and summarisation.

Transformers in Large Language Models

Most large language models use decoder-only or hybrid transformer variants. Scaling parameter count, training data, and context length has driven major capability improvements.

Transformers in Neural Machine Translation

In neural machine translation, transformers improve fluency, context consistency, and terminology control compared with earlier architectures.

Advantages Over Earlier Neural Architectures

Compared with recurrent and convolutional models, transformers provide better parallelism, stronger long-context modelling, and easier scaling on modern hardware.

Related Glossary Terms

• Transformer Architecture
• Attention Mechanism
• Large Language Models
• Neural Machine Translation
• Context Window