Positional Encoding: How Transformers Understand Word Order
Positional Encoding: How Transformers Understand Word Order
Transformers process words in parallel. But language has order. So how does the model know which word came first?
1) The Problem
Without position information, the sentence:
Dog bites man
would be identical to:
Man bites dog
2) The Solution
Add positional vectors to token embeddings. These vectors encode position using mathematical functions.
3) Why Sinusoidal Functions?
They allow the model to extrapolate to longer sequences and maintain relative position understanding.
4) Practical Insight
Modern LLMs may use learned positional embeddings or rotary positional embeddings (RoPE).
5) Summary
Positional encoding ensures meaning stays correct when words move in a sentence.
