Attention Is All You Need

Core Innovation

The 2017 paper by Vaswani et al. introduced the Transformer architecture, which revolutionized natural language processing by:

Eliminating recurrence and convolutions entirely
Relying solely on attention mechanisms
Enabling parallel processing of sequences
Capturing long-range dependencies more effectively

graph LR A[Input Sequence] --> B[Embedding Layer] B --> C[Positional Encoding] C --> D[Multi-Head Attention] D --> E[Feed Forward Network] E --> F[Output Sequence] style D fill:#7dd3fc,stroke:#333

Key Components

1. Self-Attention Mechanism

The core innovation that computes relationships between all words in a sequence simultaneously:

Attention(Q,K,V) = softmax(QKᵀ/√dₖ)V

Where:

Q (Query): What we're looking for
K (Key): What's available
V (Value): Actual content

2. Multi-Head Attention

Extends self-attention by running multiple attention mechanisms in parallel:

Allows model to focus on different positions
Learns different representation subspaces
Concatenates outputs and linearly transforms them

graph TB subgraph Multi-Head Attention A[Input] --> B[Head 1] A --> C[Head 2] A --> D[Head 3] B --> E[Concat] C --> E D --> E E --> F[Linear] end style B fill:#7dd3fc,stroke:#333 style C fill:#7dd3fc,stroke:#333 style D fill:#7dd3fc,stroke:#333

3. Positional Encoding

Since Transformers don't have recurrence, they need a way to understand word order:

PE(pos,2i) = sin(pos/10000^(2i/d_model))
PE(pos,2i+1) = cos(pos/10000^(2i/d_model))

This creates a unique positional signature for each token that the model can learn to interpret.

Transformer Architecture

graph TD A[Input Embeddings] --> B[Add Positional Encoding] B --> C[Encoder Block] C --> D[Decoder Block] D --> E[Output Probabilities] subgraph Encoder Block C1[Multi-Head Attention] --> C2[Add & Norm] C2 --> C3[Feed Forward] C3 --> C4[Add & Norm] end subgraph Decoder Block D1[Masked Multi-Head Attention] --> D2[Add & Norm] D2 --> D3[Multi-Head Attention] D3 --> D4[Add & Norm] D4 --> D5[Feed Forward] D5 --> D6[Add & Norm] end style C1 fill:#7dd3fc,stroke:#333 style D1 fill:#7dd3fc,stroke:#333 style D3 fill:#7dd3fc,stroke:#333

Encoder Stack

Processes input sequence
Contains N identical layers (typically 6)
Each layer has multi-head attention and feed-forward network
Uses residual connections and layer normalization

Decoder Stack

Generates output sequence
Also contains N identical layers
Includes masked multi-head attention to prevent looking ahead
Second attention layer attends to encoder output

Why It Matters

Parallel Processing

Unlike RNNs, Transformers process all tokens simultaneously, enabling faster training and inference.

Long-Range Dependencies

Self-attention captures relationships between all tokens regardless of distance, solving the vanishing gradient problem.

Scalability

The architecture scales remarkably well with more data and parameters, leading to models like GPT and BERT.