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.
Multi-head attention, a core component of the transformer architecture, improves model performance by allowing the model to attend to multiple aspects of the input sequence simultaneously. Each attention head learns to focus on different relationships (e.g., syntactic, semantic) in the input, capturing diverse contextual dependencies. According to "Attention is All You Need" (Vaswani et al., 2017) and NVIDIA’s NeMo documentation, multi-head attention enhances the expressive power of transformers, making them highly effective for complex NLP tasks like translation or question-answering. Option A is incorrect, as multi-head attention increases memory usage. Option C is false, as positional encodings are still required. Option D is wrong, as multi-head attention adds parameters.
[References:, Vaswani, A., et al. (2017). "Attention is All You Need.", NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html, , ]
Contribute your Thoughts:
Chosen Answer:
This is a voting comment (?). You can switch to a simple comment. It is better to Upvote an existing comment if you don't have anything to add.
Submit