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The attention mechanism is a core concept in modern neural networks, particularly within the transformer architecture. It enables a model to focus on the most relevant parts of its input when making a prediction or generating an output. Instead of treating all input elements equally, attention allows the model to assign different weights or importance scores to different elements. This is especially beneficial for sequence modeling tasks where long-range dependencies might exist.
The general idea behind attention can be understood through an analogy of an information retrieval system with Queries (Q), Keys (K), and Values (V):
- Query: Represents a request for information
- Key: Represents a descriptor or identifier of the available information
- Value: Represents the actual information content
The attention process typically involves:
- Calculating a similarity score between the Query and each Key
- Normalizing these scores to obtain attention weights (usually using
softmax) - Computing a weighted sum of the Values based on these attention weights
The standard attention mechanism (which is widely popular) is the dot product attention which computies scores as the dot product of query and key vectors:
There are numerous variants of attention mechanism, each tailored to specific needs: computational efficiency, model capacity, or task constraints. Few popular variants are:
- scaled dot product attention: Same as standard, but with scaling of scores for stability
- multi-head attention: Parallel attention heads for diverse feature learning. Works by concatenating outputs from multiple scaled dot product attention “heads”
- self-attention: Q,K,V from same sequence (internal dependencies)
- cross-attention: Q comes from one sequence and K, V from aonther
- masked self-attention: Self-attention with future-position masking