Which of the following best defines the 'Decoder' in a Transformer model?

The 'Decoder' in a Transformer model is best defined as an autoregressive module that generates output sequences. In the architecture of Transformers, the Decoder operates by taking input sequences, often from the preceding steps of the sequence generated, along with context supplied by the Encoder. Its autoregressive nature means that it generates output tokens one at a time, using previously generated tokens to influence the generation of the next token. This is essential for tasks such as machine translation or text generation, where the sequence should be coherent and relevant to the context.

The role of the Decoder is crucial because it allows for the modeling of relationships between previously generated tokens and the new outputs, taking into account any attention mechanisms that prioritize certain parts of the input data based on relevance. This ability to generate sequences in a stepwise fashion while maintaining contextual awareness is what distinguishes it from other components in the model, such as the Encoder, which primarily focuses on understanding and representing the input data rather than generating output.