The decoder is the part of the Transformer that generates output sequences, usually token by token. It relies on self-attention to reference previous outputs and cross-attention to use encoder context. Although the article centers on self-attention, it notes how decoders apply it differently due to masking. Masked self-attention prevents access to future tokens during generation. Decoders are crucial in tasks like translation and summarization. They showcase the versatility of attention across input and output. Understanding the decoder helps complete the picture of how attention is applied.