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

Which mechanism in transformers addresses the quadratic complexity of self-attention?Group of answer choicesSparse attentionLayer normalizationMulti-head attentionPositional encoding

What is the primary function of the self-attention mechanism in transformers?Group of answer choicesTo perform backpropagationTo reduce the computational costTo reduce the computational cost of trainingTo allow the model to weigh the importance of different words in a sentence relative to each other

Which of the following is NOT a core component of the Transformer self-attention mechanism?Question 5Answera.Convolutional Layerb.Query Vectorc.Key Vectord.Value Vector

How is the final attention output computed using the attention weights and value vectors?<br /> A. a. By taking the dot product of the attention weights and value vectors <br />B. b. By concatenating the attention weights and value vectors <br />C. c. By taking a weighted sum of the value vectors using the attention weights <br />D. d. By adding the attention weights to the value vectors element-wise

How does the attention mechanism in the Transformer architecture benefit sentiment analysis tasks?Question 2Answera.By decreasing the training timeb.By focusing on important words and phrases in the text, irrespective of their positionc.By reducing the model sized.By improving the interpretability of the model