In the context of machine learning, what is the purpose of self-attention mechanisms in Transformers?Question 17Answera.Self-attention assists in computing certain functions in machine learning algorithmsb. Self-attention enables efficient exploration of the in put spacec. Self-attention is used to determine specific strategies in machine learning tasksd. Self-attention helps in selecting relevant parts of the input sequence for processing

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

3.Question 3What is the self-attention that powers the transformer architecture?1 pointA mechanism that allows a model to focus on different parts of the input sequence during computation.A technique used to improve the generalization capabilities of a model by training it on diverse datasets.A measure of how well a model can understand and generate human-like language.The ability of the transformer to analyze its own performance and make adjustments accordingly.4.Question 4

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

(1) Self-attention is a mechanism used in deep learning. Which of the following descriptions about self-attention is correct?Self-attention是一項深度學習的機制，下列哪個有關 self-attention 的描述是正確的？Self-attention is not used for text processing. Self-attention 不會用於文字處理。Self-attention mechanism in Transformer does not consider the adjacent tokens in a sentence. Transformer中的Self-attention機制不會考慮句子中相連的Token (詞元)。Self-attention mechanism can extract relations of tokens in the input. Self-attention機制能夠在輸入的Token(詞元)中抽取出關係。Self-attention mechanism in Transformer only considers the adjacent tokens in a sentence. Transformer中的Self-attention機制只考慮句子中相連的Token(詞元)。

In a Transformer decoder, what is the purpose of the masked self-attention layer?Question 2Answera.Assign weights to relevant parts of the input sequence.b.None of thesec.Generate a representation of the entire output sequence.d.Allow the model to "attend" to previously generated tokens.