Attention Is All You Need, Explained Without the Math

Everyone cites this paper. Few have read it.

"Attention Is All You Need" (Vaswani et al., 2017) introduced the Transformer architecture. GPT, Claude, Llama—all descendants.

Here's what the paper actually says, in terms a programmer can use.

The Problem They Solved

Before 2017, sequence processing meant RNNs (Recurrent Neural Networks). Process tokens one at a time, pass state forward.

Token 1 → Process → State₁
Token 2 + State₁ → Process → State₂
Token 3 + State₂ → Process → State₃
...

Two problems:

1. Sequential processing - Can't parallelize. Token 5 waits for tokens 1-4.
2. Long-range dependencies - By token 100, information from token 1 is diluted through 99 state updates.

The Insight

What if every token could see every other token directly?

Not "pass information through a chain." Direct access. Token 100 looks at token 1 with the same ease as token 99.

That's attention.

How Attention Works (Programmer's Model)

Think of it as a lookup table with fuzzy matching.

You have a query: "What's relevant to understanding this word?"

You have keys: Every other word in the sequence, tagged with "here's what I offer."

You have values: The actual information each word contributes.

Input: "The cat sat on the mat"

Processing "sat":
  Query: "What context helps understand 'sat'?"

  Keys (what each word offers):
    "The" → [article, beginning]
    "cat" → [subject, animal, noun]
    "sat" → [self]
    "on"  → [preposition, location]
    "the" → [article]
    "mat" → [object, location, noun]

  Attention weights (how relevant is each):
    "The" → 0.05
    "cat" → 0.40  ← Subject of "sat"
    "sat" → 0.10
    "on"  → 0.20  ← Modifies "sat"
    "the" → 0.05
    "mat" → 0.20  ← Object of action

  Output: Weighted combination of all values

"sat" pays most attention to "cat" (what sat?) and "mat"/"on" (where/how sat?).

This happens in parallel for every token. No sequential dependencies.

Multi-Head Attention

One attention pattern isn't enough.

"sat" might need to know:

• Who performed the action (subject)
• What was affected (object)
• Where it happened (location)
• When (tense/temporal)

Multi-head attention runs multiple attention patterns in parallel, each learning different relationship types.

Head 1: Subject-verb relationships
Head 2: Verb-object relationships
Head 3: Positional relationships
Head 4: Syntactic relationships
...

The paper used 8 heads. Modern models use 32-128.

Self-Attention vs Cross-Attention

Self-attention: Sequence attends to itself. Every token looks at every other token in the same sequence. Used for understanding input.

Cross-attention: One sequence attends to another. The decoder attends to encoder output. Used for generation based on input.

Self-attention (encoder):
  "The cat sat" → each word attends to all words in "The cat sat"

Cross-attention (decoder):
  Generating translation → attends to encoded source sentence

The Transformer Architecture

Stack these components:

ENCODER (understanding input):
┌──────────────────────────┐
│ Input Embedding          │
├──────────────────────────┤
│ + Positional Encoding    │ ← "sat" is at position 3
├──────────────────────────┤
│ Multi-Head Self-Attention│
├──────────────────────────┤
│ Feed-Forward Network     │
├──────────────────────────┤
│ (Repeat N times)         │
└──────────────────────────┘

DECODER (generating output):
┌──────────────────────────┐
│ Output Embedding         │
├──────────────────────────┤
│ + Positional Encoding    │
├──────────────────────────┤
│ Masked Self-Attention    │ ← Can only see previous tokens
├──────────────────────────┤
│ Cross-Attention          │ ← Attends to encoder output
├──────────────────────────┤
│ Feed-Forward Network     │
├──────────────────────────┤
│ (Repeat N times)         │
└──────────────────────────┘

The original paper: N=6 layers, 8 attention heads, 512-dimensional embeddings.

GPT-4 (rumored): ~120 layers, 96 heads, 12,288+ dimensions.

Why "All You Need"?

The paper's claim was bold: attention alone, without recurrence or convolution, is sufficient for sequence modeling.

Results backed it up:

Better quality. Fraction of the training time. Parallelizable.

Positional Encoding

Attention has no inherent notion of order. "cat sat mat" and "mat sat cat" produce the same attention patterns without positional information.

The paper added position via sine/cosine functions:

Position 0: [sin(0), cos(0), sin(0), cos(0), ...]
Position 1: [sin(1/10000), cos(1/10000), ...]
Position 2: [sin(2/10000), cos(2/10000), ...]

This creates unique, learnable patterns for each position. Modern models often use learned positional embeddings instead.

What Surprised the Authors

From the paper's analysis section:

1. Attention heads specialize. Different heads learn syntax vs semantics vs position.
2. Long-range attention works. Heads successfully attend across 20+ tokens.
3. Attention is interpretable. You can visualize what the model "looks at."

What It Enabled

The Transformer architecture, unchanged in principle, powers:

• GPT series - Decoder-only, autoregressive
• BERT - Encoder-only, bidirectional
• T5 - Encoder-decoder, text-to-text
• Vision Transformers - Images as token sequences
• Whisper - Audio as token sequences

The paper didn't invent attention (that was 2014). It showed attention was sufficient—no RNNs, no convolutions, just attention and feed-forward layers.

Key Takeaways

1. Attention is parallel lookup. Every token queries every other token simultaneously.
2. Multi-head = multiple relationship types. Different heads learn different patterns.
3. Position must be added. Attention itself is order-agnostic.
4. Scaling is the unlock. The architecture supports massive parallelization.

One paper. 2017. Still the foundation.

Understanding attention isn't about the math. It's about the mechanism: let every part of the input talk to every other part, directly, in parallel.

That's why it's all you need.