NCA-GENL Question 6
Single answerA team is training a generative AI model to predict the next word in a sentence. They decide to use a Transformer-based architecture and apply a masked language modeling approach. Which of the following best describes the role of the attention mechanism in this context?
- A
The attention mechanism identifies the most relevant parts of the input sequence for the current prediction.
- B
The attention mechanism ensures all tokens in the input sequence have equal importance for the output.
- C
The attention mechanism reduces the size of the input data by compressing it into a fixed-length vector.
- D
The attention mechanism applies random noise to the input sequence to improve generalization.
Show answer and explanation
Correct answer: A
Explanation
The attention mechanism in Transformer models is a core component that computes the relevance of each token in the input sequence with respect to the others. In tasks like masked language modeling, this allows the model to focus on the most important parts of the sentence to predict the next word accurately. This dynamic weighting of input tokens is what makes attention mechanisms highly effective for generative AI tasks.
- A. Correct.
The attention mechanism identifies the most relevant parts of the input sequence for the current prediction, which is why Transformers excel in tasks like language modeling.
- B. Incorrect.
This is incorrect because the attention mechanism does not treat all tokens equally; it assigns different weights to different tokens based on their relevance.
- C. Incorrect.
This is incorrect as the attention mechanism does not compress the input. Instead, it dynamically computes relationships between all tokens in the sequence.
- D. Incorrect.
This is incorrect because adding random noise is not the function of the attention mechanism. Random noise might be added in other techniques, such as data augmentation, but not in attention.