What is Attention Mechanism?
An attention mechanism is a neural network component that allows models to dynamically focus on relevant parts of their input when processing information. Rather than treating all input elements equally, attention mechanisms compute weights that determine how much "attention" the model should pay to each part of the input when producing each part of the output. This selective focus enables models to handle variable-length inputs and capture long-range dependencies more effectively.
The core idea behind attention is to create a weighted combination of input representations, where the weights are learned based on the relevance of each input element to the current processing task. For example, when translating a sentence, the attention mechanism helps the model focus on the relevant words in the source language when generating each word in the target language. These weights are typically computed using similarity measures between query and key vectors, producing attention scores that are normalized and used to weight value vectors.
Attention mechanisms revolutionized natural language processing and are the foundation of transformer architectures that power modern large language models. They enable models to process sequences without the recurrence limitations of earlier architectures, allowing for parallel processing and better handling of long-range dependencies. Variants like self-attention and multi-head attention have become essential building blocks in state-of-the-art AI systems.