What is the Attention Mechanism? - Definition & Meaning

Definition

The attention mechanism is a technique where a model learns to assign different weights to other positions when processing each position. It determines "what the model should pay attention to" for the current task.

Technical explanation

Attention computes a weighted sum of values based on scores between queries and keys. Scaled dot-product attention: score = softmax(QK^T / √d). Multi-head attention applies multiple attention layers in parallel with different projections, learning different types of relationships. Self-attention uses the same sequence as query, key, and value. Cross-attention connects encoder and decoder sequences. Attention enables long-range dependencies and context-dependent representations — the reason Transformers are so effective.

How AVARC Solutions applies this

AVARC Solutions builds AI that leverages attention under the hood (via LLMs and transformer models). We design prompts and RAG pipelines that optimally use available context so the attention mechanism can effectively select relevant information for the answer.

Practical examples

• A translation model using attention to determine which source word is most relevant for each target word.
• A question-answering model using attention to select the most relevant passages from a document for the answer.
• A code assistant using attention to identify related functions and variables in the context.

Related terms