What is ‘positional encoding’ in transformer models and why is it necessary?

Question

AI Fundamentals — Hard

What is ‘positional encoding’ in transformer models and why is it necessary?

Accepted Answer

Positional encoding in transformer models involves adding fixed or learned vectors to token embeddings to capture sequence order information. This is necessary because self-attention is permutation-invariant, so positional encoding helps the model understand the order of tokens in a sequence.