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What are queries keys and values in Transformers?
The meaning of query, value and key depend on the application. In the case of text similarity, for example, query is the sequence embeddings of the first piece of text and value is the sequence embeddings of the second piece of text. key is usually the same tensor as value.
Why is it necessary for the dimensionality of queries and keys to be the same?
The dimension of query and key must always be the same because of the dot product score function. However, the dimension of value may be different from query and key. The resulting output will consequently follow the dimension of value.
What is a query in transformer?
The query transformer is a component of the query optimizer and take as input a parsed query, which is represented by a set of query blocks. The query blocks are nested or interrelated to each other. The form of the query determines how the query blocks are interrelated to each other.
How are key and value matrices defined in Transformers?
The key and value matrices are learned parameters while the query matrix is defined by the input word vectors. It is also important to note that the words of a sentence are passed into the transformer at the same time and the concept of a sequential order present in LSTMs is not that apparent with transformers.
Where do keys, queries, and values come from?
The key/value/query concepts come from retrieval systems. For example, when you type a query to search for some video on Youtube, the search engine will map your query against a set of keys (video title, description, etc.) associated with candidate videos in the database, then present you the best matched videos (values).
Which is part of the transformer chooses what to focus on?
The encoder is the part of the transformer that chooses what parts of the input to focus on. The encoder can take a sentence such as “the quick brown fox jumped”, computes the embedding matrix, and then converts it into a series of attention vectors.
How are keys, queries and values learned in attention?
By multiplying an input vector with a matrix V (from the SVD), we obtain a better representation for computing the compatibility between two vectors, if these two vectors are similar in the topic space as shown in the example in the figure. And these matrices for transformation can be learned in a neural network!