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 prevent the model from attending to padding tokens, a padding mask is used. The padding mask is a boolean matrix that indicates which tokens are padding tokens and which are real words. This mask is applied to the attention scores before the softmax function is applied, setting the scores for padding tokens to a very large negative value (e.g., -infinity). This ensures that the attention weights for padding tokens are effectively zero, preventing them from contributing to the weighted sum of value vectors. For output sequences, the Transformer handles variable lengths through an autoregressive decoding process. The decoder generates the output sequence one token at a time, conditioned on the previously generated tokens and the encoder output. This process continues until the model generates a special end-of-sentence token (`<EOS>`) or reaches a maximum sequence length. Future masking (also known as causal masking) is used during training to prevent the decoder from "cheating" by attending to future tokens in the output sequence when predicting the current token. This ensures that the decoder learns to generate the output sequence in an autoregressive manner, which is consistent with how it will be used during inference. Together, these techniques allow the Transformer architecture to effectively handle variable-length input and output sequences, making it well-suited for tasks such as machine translation, text summarization, and text generation.