AI Blog

AI Blog

by Michele Laurelli

Attention Weight

/əˈtɛnʃən weɪt/
Concept
Definition

A scalar value indicating how much focus to place on a specific part of the input when producing output.

Attention weights are computed through dot products of query and key vectors, followed by softmax normalization. Higher weights mean greater importance. They enable models to focus on relevant information dynamically.

Examples

1

Translation attention to source words

2

Image captioning attention to regions

3

Document summarization

Michele Laurelli - AI Research & Engineering