Question: What is the primary role of the self-attention mechanism in the Transformer architecture?

A) To enhance the model’s ability to process sequential data in order.
B) To allow the model to focus on relevant parts of the input sequence when making predictions.
C) To replace recurrent connections and reduce computation time.
D) To normalize the weights in each layer for faster convergence.

Correct Answer:
B) To allow the model to focus on relevant parts of the input sequence when making predictions.

Explanation:
The self-attention mechanism enables the Transformer to weigh the importance of each token relative to others in the sequence. This allows the model to understand context and relationships, even for tokens far apart, which is critical for tasks like language understanding and generation.