How does the attention mechanism work?

The attention mechanism is a neural network component that dynamically weights input elements based on context, enhancing model focus on relevant parts. It enables models to selectively concentrate on critical information during processing.

It operates by assigning an "importance score" to each input element (e.g., words, features) relative to the current processing step. These scores are computed using a learnable function comparing a query vector (representing the current state) with key vectors (representing input elements). Softmax converts scores to weights, creating a weighted sum of value vectors—the context vector. This allows prioritizing pertinent information over irrelevant data across diverse inputs.

Implementation involves: 1) Generating query, key, and value vectors; 2) Calculating compatibility scores (e.g., dot-product, additive); 3) Applying softmax to get weights; 4) Summing weighted values into a context vector. Primarily used in NLP (machine translation, text summarization) and vision, it drastically improves long-sequence handling, interpretability, and model performance by focusing computational resources contextually.