Self-Attention
A mechanism employed in deep learning models that allows a model to weigh the importance of different parts of an input sequence when processing each element of that sequence
Overview
A mechanism employed in deep learning models, particularly in the Transformer architecture, that allows the model to weigh the importance of different parts of an input sequence when processing each element of that sequence. By computing relationships between all elements within the sequence, self-attention enables the model to capture long-range dependencies and contextual information effectively.
What is Self-Attention?
Self-attention is a mechanism that allows models to weigh the importance of the relationship between elements in an input sequence. It enables each element in a sequence to interact with every other element, creating a rich understanding of context and relationships within the data.
How Does it Work?
Models compute relationships between all elements within the sequence, allowing the model to capture complex long-range dependencies. This is achieved by calculating attention scores between each pair of elements in the sequence, which determines how much focus should be placed on different parts of the input when processing each element.
Why is it Important?
Self-attention has been instrumental in advancing the state-of-the-art in natural language processing. Its ability to handle long-range dependencies and capture contextual relationships has made it a fundamental component in modern language models and other sequence processing tasks.