In self-attention mechanisms, the query ($Q$) is an important component because it determines which parts of the input sequence should be attended to.
- The query is derived from the current input, and it is used to calculate the attention scores with the key matrix ($K$),
- which are then used to weight the values ($V$) and compute the output of the attention layer.
The query vector is typically a learned parameter that is updated during training to optimize the performance of the model.
Advantages
By learning which parts of the input sequence to attend to, the model can better capture the relationships between the different elements of the input and make more accurate predictions.
How to calculate
Here is the equation for the query vector in a self-attention layer:
$$ Q = XW_Q $$
where $X$ is the input sequence of embeddings, and $W_Q$ is a learned weight matrix that transforms the input embeddings into the query vector.
The query vector is a projection of the input embeddings into a lower-dimensional space, which allows the model to capture the most important aspects of the input sequence while discarding irrelevant information.
- The attention scores are then computed by taking the dot product between the query vector and the key matrix,
- and the output of the self-attention layer is a weighted sum of the value matrix, = where the weights are given by the attention scores.
In summary, the query vector is an important component of the self-attention mechanism because it determines which parts of the input sequence to attend to, and it is learned during training to optimize the performance of the model.