What is Positional Encoding in Transformers and why is it necessary?

Positional encoding injects information about token order into Transformer models since self-attention alone is permutation-invariant.

Unlike RNNs, Transformers process tokens in parallel, which means they do not inherently understand sequence order.

Without positional information:

Positional encoding solves this by adding order-aware signals to embeddings.

Types:

Formula: PE(pos, 2i) = sin(pos / 10000^(2i/d)) PE(pos, 2i+1) = cos(pos / 10000^(2i/d))

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