Attention scores in transformers are computed using the dot product of the query and key vectors.Group of answer choicesTrueFalse

Answer

The statement is True.

In transformer models, attention scores are indeed computed using the dot product of the query and key vectors. This process allows the model to assess the relevance between different tokens in the sequence. The attention mechanism transforms the queries and keys into a similarity score, typically scaled by the square root of the dimension of the key vectors to stabilize gradients. The resulting scores are then passed through a softmax function to derive the attention weights, which are applied to the value vectors to obtain the final output. Thus, the mechanism efficiently determines how much focus to place on different parts of the input sequence.

In summary, the statement is true based on the fundamental workings of the attention mechanism in transformers.