AI Glossaryfundamentals

Transformer

The neural network architecture behind all modern LLMs, using self-attention mechanisms to process sequences in parallel.

How It Works

Introduced in the 2017 paper "Attention Is All You Need," transformers replaced older recurrent architectures (RNNs, LSTMs) by processing entire sequences simultaneously using attention mechanisms. This parallelism made training on massive datasets feasible. Every major AI model today (GPT, Claude, Gemini, Llama) is based on the transformer architecture.

Common Use Cases

  • 1Language understanding
  • 2Text generation
  • 3Image recognition (Vision Transformers)
  • 4Audio processing

Related Terms

Large Language Model (LLM)

A neural network trained on massive text datasets that can generate, understand, and reason about human language.

Tokenization

The process of breaking text into smaller units (tokens) that an AI model can process, typically subwords or word pieces.

Attention Mechanism

A neural network component that allows models to dynamically focus on the most relevant parts of the input when generating each token of output.

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