Multi-Head Attention Mechanism

A Multi-Head Attention Mechanism is an attention mechanism that includes simultaneous attention to information from different representation subspaces at different positions.

Context:
- It can allow models to capture a richer understanding of the input by attending to it in multiple "ways" or "aspects" simultaneously, significantly improving the model's ability to handle complex tasks such as language translation, document summarization, and question-answering.
- It can enable the model to disentangle various types of relationships within the data, such as syntactic and semantic dependencies in text, by dedicating different "heads" to focus on different types of information.
- It can (often) be combined with other mechanisms, such as position encoding and layer normalization, to further enhance model performance and training stability.
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Example(s):
- as proposed in (Vaswani et al., 2017).
- as proposed in (Devlin et al., 2018).
- as proposed in (Brown et al., 2020).
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Counter-Example(s):
- A Single-Head Attention Mechanism in a neural network, which can only focus on one aspect of the information at any given time.
- A Convolutional Layers in neural networks, which apply the same filters across all parts of the input without dynamic focusing.
See: Transformer architecture, attention mechanism, neural network component, representation subspace, position encoding, layer normalization.

References

(Vaswani et al., 2017) ⇒ Ashish Vaswani, Noam Shazeer, ..., Łukasz Kaiser, and Illia Polosukhin. (2017). “Attention Is All You Need.” In: Advances in Neural Information Processing Systems, 30 (NeurIPS 2017). arXiv:1706.03762
- NOTE: Introduced the concept of Multi-Head Attention Mechanism as a means to allow the model to jointly attend to information from different representation subspaces at different positions, enhancing the ability to capture complex input relationships .
- NOTE: The mechanism projects queries, keys, and values multiple times with different, learned linear projections, enabling parallel processing of attention which significantly contributes to both efficiency and model performance .

(Devlin et al., 2018) ⇒ Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. (2018). “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.” arXiv preprint arXiv:1810.04805.
- NOTE: Utilizes Multi-Head Attention Mechanism within the Transformer model to process both left and right context of a token simultaneously, significantly improving language understanding by capturing a richer context .

(Brown et al., 2020) ⇒ Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, ..., Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. (2020). “Language Models are Few-Shot Learners.” In: Advances in Neural Information Processing Systems. arXiv preprint arXiv:2005.14165.
- NOTE: Demonstrated the scalability of the Multi-Head Attention Mechanism in the GPT-3 model, which processes vast amounts of text to generate highly contextualized responses across a wide range of tasks.