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
Question
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
Solution
The "masking" mechanism in transformer-based language models is significant primarily for reason d. It prevents the model from attending to future tokens during training. This is crucial in language modeling tasks because it ensures that the prediction for each word is dependent only on the words that precede it, and not on any words that come after it. This is how humans read and understand text - we don't know what the next word in a sentence will be until we've read it. Therefore, to train a model to understand and generate text in a similar way, we use a mask to hide future words from it during training. This is often referred to as "causal masking" or "autoregressive masking".
While options a, b, and c could potentially be effects of different types of masking or attention mechanisms, they are not the primary purpose or most significant aspect of masking in transformer models.
Similar Questions
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.
Question 6Which transformer-based model architecture has the objective of guessing a masked token based on the previous sequence of tokens by building bidirectional representations of the input sequence.1 pointAutoencoderSequence-to-sequenceAutoregressive
Masking is usedQuestion 2Answera.to manipulate the extent to which an observer is aware of a stimulus.b.to bias an observer to perceive a stimulus in a particular way.c.to prevent a participant from using visual cues in an experiment on auditory perception.d.to prime a participant prior to the onset of a target stimulus.
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 Transformers, what is the role of positional encoding?Question 8Answera.Capture the order of words in a sequence.b.Represent the meaning of individual words.c.None of thesed.Improve the efficiency of the self-attention mechanism.
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