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Q4 Single choice

In transformer-based LLMs, how does the use of multi-head attention improve model performance compared to single-head attention, particularly for complex NLP tasks?

A
Multi-head attention reduces the model's memory footprint by sharing weights across heads.
B
Multi-head attention allows the model to focus on multiple aspects of the input sequence simultaneously.
C
Multi-head attention eliminates the need for positional encodings in the input sequence.
D
Multi-head attention simplifies the training process by reducing the number of parameters.

NCA-GENL Web TestEngine demo