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.

In transformer-based language models, what is the significance of the “masking” mechanism ?Question 12Answera. It masks out irrelevant parts of the input sequence to reduce computationb. It allows the model to prioritize certain tokens based on their position in the sequencec.It ensures that rare tokens are given higher attention weightsd.It prevents the model from attending to future tokens during training

What is the main role of the decoder in a Transformer model?Question 14Answera.To generate output tokens based on the final encoder representation.b.To compute attention scores between input and output tokens.c.Learning positional encodings.d.To encode the input sequence.

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

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