What is the role of the query matrix in the self-attention mechanism?

The query matrix in the self-attention mechanism represents the "question" or the "search term" that each word in the input sequence is posing to the other words in the sequence. Each word's embedding is transformed into a query vector by multiplying it with the query matrix, which is a learned weight matrix. This query vector is then used to compare the word to all other words in the sequence, determining how relevant each other word is to the current word. In essence, the query matrix transforms the word embedding into a representation that is specifically designed for the purpose of measuring similarity with other words. The learned parameters within the query matrix allow the model to focus on different aspects of each word when determining its relevance to other words. The query matrix allows the model to learn complex patterns in the data and to capture nuanced relationships between words. For instance, a word's query vector could encode its syntactic role, its semantic meaning, or its relationship to other entities in the text. The query matrix is a crucial component of the self-attention mechanism, as it allows the model to selectively attend to different parts of the input sequence and to generate context-aware representations.