The Transformer architecture, known for revolutionizing natural language processing tasks, employs a powerful mechanism called multi-headed self-attention. This technique allows the model to focus on different aspects of language simultaneously, leading to richer and more nuanced representations.
Concept:
Instead of a single set of attention weights, the model learns multiple sets in parallel, each called a head.
Each head focuses on different aspects of the input sequence, independently of other heads.
The number of heads varies across models, typically ranging from 12 to 100.
Benefits:
Captures diverse relationships: Each head learns specific patterns and relationships within the input, enabling the model to capture various linguistic features.
Improves representation: The combined output of all heads provides a more comprehensive and nuanced representation of the input, allowing for better performance on downstream tasks.
Example:
Head 1: Focuses on identifying relationships between people entities in the sentence.
Head 2: Concentrates on understanding the activity being described.
Head 3: Analyzes other aspects, like rhyme scheme or figurative language.
Learning Process:
The model automatically learns what aspects each head focuses on based on the training data.
This enables the model to adapt and capture relevant information for different types of text.
See Also: Attention, Transformers