The Attention Mechanism Explained Visually

Attention is the core innovation behind transformers. Let me break it down without the math jargon.

The Intuition

Imagine reading a sentence: "The cat sat on the mat because it was tired."

When you read "it," your brain instantly connects it to "cat." That's attention—selectively focusing on relevant parts of the input.

How It Works

text
Input: "The cat sat on the mat"
       ↓
   [Query] [Key] [Value]
       ↓
   Attention Scores
       ↓
   Weighted Output

Step 1: Create Q, K, V

Each word gets three representations:

• Query (Q): "What am I looking for?"
• Key (K): "What do I contain?"
• Value (V): "What information do I provide?"

Step 2: Calculate Scores

python
scores = Q @ K.T / sqrt(d_k)  # Scaled dot product
attention_weights = softmax(scores)
output = attention_weights @ V

Step 3: Weighted Combination

The output is a weighted sum of values, where weights come from how well queries match keys.

Multi-Head Attention

Instead of one attention operation, transformers use multiple "heads":

text
Head 1: Focuses on syntax
Head 2: Focuses on semantics  
Head 3: Focuses on position
...

This lets the model capture different types of relationships simultaneously.

Why It Matters

Attention enables:

• Long-range dependencies (unlike RNNs)
• Parallel processing (unlike sequential models)
• Interpretability (you can visualize what attends to what)

Practical Example

In translation, when generating "chat" (French for cat):

• High attention on "cat" (semantic match)
• Some attention on "the" (article agreement)
• Low attention on "mat" (irrelevant)

This selective focus is what makes modern NLP work.