How does the Transformer architecture handle variable-length input and output sequences?

The Transformer architecture handles variable-length input and output sequences through a combination of padding, masking, and its inherent architecture based on self-attention. Because neural networks typically process data in fixed-size batches, input sequences of varying lengths must be padded to a uniform length within each batch. Padding involves adding special tokens (usually denoted as `<PAD>`) to the end of shorter sequences to make them the same length as the longest sequence in the batch. However, these padding tokens do not contain any meaningful information and should not influence the attention weights. To p....