#attention In the literature of the Machine Learning, Cross Attention refers to a specific type of attention mechanism used in the transformer architecture. The transformer model, introduced in the paper "Attention is All You Need" by Vaswani et al., revolutionized various natural language processing tasks. The attention mechanism in transformers allows the model to focus on important parts of the input sequence when processing each token. In traditional self-attention, also known as intra-attention, an input sequence attends to itself to capture dependencies within the sequence. However, in certain scenarios, it is essential for one input sequence to attend to another related sequence. This is where Cross Attention comes into play. Cross Attention enables a transformer model to attend not only to its own input but also to another related input sequence. It introduces an additional attention mechanism that allows information exchange between different sequences. In the transformer architecture, Cross Attention is typically used in encoder-decoder models for tasks such as machine translation or text summarization. The encoder processes the source language sentence and creates contextualized representations for each token using self-attention. In addition to self-attention, the decoder also employs cross-attention to attend over these source representations while generating the target language sentence. During cross-attention, each token in the decoder attends to all tokens in the encoder based on their relevance and importance for generating a specific output token. This enables effective information transfer from the source sequence (encoder) to guide the generation process of target tokens (decoder). Cross Attention has proven to be highly effective in capturing dependencies between different sequences and has significantly improved performance on various natural language processing tasks compared to previous models that lacked this mechanism. ## How does cross attention compares to self-attention Cross attention is a type of attention mechanism used in transformers where information from one sequence is attended to by another sequence. This is in contrast to self-attention, where the attention mechanism attends to different positions within the same sequence. The main difference between cross attention and self-attention lies in the input and output dimensions. In self-attention, the input and output sequences are the same, while in cross attention, the input and output sequences can be different. In self-attention, each position in the input sequence attends to all other positions within the same sequence. This allows for capturing dependencies between different positions within the sequence and enables modeling relationships between words or elements that are far apart. Self-attention is symmetric because every position contributes equally to attending over other positions. On the other hand, cross attention allows information from one sequence (query) to attend to another sequence (key-value) during encoding or decoding tasks. It enables capturing dependencies between different sequences or capturing relationships between elements of two different sequences. Cross attention is not symmetric because there is an asymmetry between queries and keys. Furthermore, cross attention can be used for tasks such as machine translation, where a source language sentence attends to a target language sentence during encoding and decoding phases respectively. It helps align relevant source words with target words for translation. In summary, while both cross attention and self-attention are mechanisms that allow capturing dependencies between elements within or across sequences, cross attention specifically focuses on attending information from one sequence to another while self-attention captures dependencies within a single sequence. # Conclusion Overall, Cross Attention plays a crucial role in enabling transformers to effectively capture relationships between multiple sequences and has become a fundamental component of state-of-the-art machine learning models for various natural language processing tasks. # References ```dataview Table title as Title, authors as Authors where contains(subject, "Attention") ```