Cross-attention

Attention where one sequence looks at another, such as a translation model's decoder reading the encoder's output.

The encoder reads the source sentence, with attention in both directions. The decoder writes the translation one token at a time, with a causal mask. To look back at the source, the decoder uses cross-attention. The queries come from the decoder. The keys and values come from the encoder's outputs. So each word being written can look up the parts of the source it needs.

Other terms from the same lesson

• BERT: Google's 2018 encoder model, trained by hiding words and guessing them from both sides. Its descendants still power much search and classification.
• bidirectional: Reading in both directions: each token can look at the words before and after it, as encoders like BERT do.
• contrastive learning: Training that pulls the embeddings of matching pairs (like a question and its answer) together and pushes mismatched pairs apart.
• cross-encoder: A model that reads a query and a document together and gives one relevance score. More accurate than comparing embeddings but slower, so it reranks a short list.
• encoder: A transformer that reads the whole text at once to understand it, used for embeddings and classification.
• masked language modeling: Training by hiding some tokens and asking the model to guess them from the words on both sides. It's how BERT learned.
• mean pooling: Turning a text's token vectors into one vector for the whole text by averaging them.