Position Embeddings Explorer

🔄 The Position Blindness Problem

Attention mechanism by itself is position-blind - it treats "John loves Mary" exactly the same as "Mary loves John". This is a fundamental problem that position embeddings solve.

❌ Without Position Info

John loves Mary

Attention sees: {John, loves, Mary}
Same as: {Mary, loves, John}

Problem: Can't tell who loves whom!

✅ With Position Embeddings

John₁ loves₂ Mary₃

Attention sees: {John@pos1, loves@pos2, Mary@pos3}
Different from: {Mary@pos1, loves@pos2, John@pos3}

Solution: Position makes order meaningful!

🎲 Interactive Position Demonstration

See Position Blindness in Action

Without position embeddings, these sentences would be identical to the attention mechanism:

The cat chased the dog

Try shuffling the words - without position embeddings, attention can't tell the difference between any arrangement!

📊 Position Encoding Patterns

Each position gets a unique "fingerprint" that helps attention understand word order:

Each position has a unique pattern of values. Hover over positions to see their encoding!

🔍 How Position Embeddings Work

Simple Formula:

                    Final Embedding = Word Embedding + Position Embedding
                

Word Embedding
What the word means
"cat" → [0.2, -0.1, 0.8, ...]

Position Embedding
Where the word appears
pos2 → [0.0, 0.5, -0.3, ...]

Final Embedding
Word + Position
[0.2, 0.4, 0.5, ...]

Key Insights

Position Blindness: Pure attention can't distinguish word order - "John loves Mary" = "Mary loves John"
Simple Solution: Add position embeddings to word embeddings before attention
Unique Fingerprints: Each position gets a unique pattern that attention can learn to use
Order Preservation: Now attention can understand that position 1 comes before position 2
Learned Relationships: The model learns that subjects usually come before verbs, etc.