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Positional encoding helps to identify the sequence of data inputs for the encoder, which then directly sends its output to the decoder.
The encoder processes the data without needing positional encoding, while the decoder only uses it to predict future outputs.
Both the encoder and decoder use positional encoding to retain the order of data, allowing for more accurate context understanding during processing.
Positional encoding is only relevant in the decoder phase and has no role in the encoder structure.
Understanding the Answer
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Both the encoder and decoder add positional encoding to every token. Other options are incorrect because The idea that only the encoder needs positional encoding is a misconception; This option ignores that the encoder also uses positional encoding.
Key Concepts
Transformer Architecture
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Definition
The Transformer is a network architecture based solely on attention mechanisms, eliminating the need for recurrent or convolutional layers. It connects encoder and decoder through attention, enabling parallelization and faster training. The model has shown superior performance in machine translation tasks.
Topic Definition
The Transformer is a network architecture based solely on attention mechanisms, eliminating the need for recurrent or convolutional layers. It connects encoder and decoder through attention, enabling parallelization and faster training. The model has shown superior performance in machine translation tasks.
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