How does positional encoding work in transformers?
This tests your understanding of why Transformers need explicit position data. A great answer explains that self-attention is permutation-invariant, meaning it sees inputs as an unordered set. Positional encodings—vectors derived from sine and cosine functions—are then added to the input embeddings to inject sequence order. A red flag is simply saying 'it adds position' without explaining why this is necessary or how it's done.
This question tests your understanding of why a Transformer, unlike an RNN, needs an explicit mechanism for sequence order. A strong answer starts by explaining the core problem: the self-attention mechanism is permutation-invariant and processes tokens in parallel, losing their original order. You should then describe the solution: a unique positional encoding vector is generated for each position in the sequence. This vector is added to the corresponding token's embedding, enriching it with positional context. Mentioning the original paper's use of sine and cosine functions is a plus. A common red flag is failing to explain the 'why'—the permutation-invariance of attention—and just stating that it adds position.
Read the original → Wikipedia: Transformer (deep learning architecture)
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