Attention Mechanism in Transformer Models

Overview

The attention mechanism in Transformer models is a revolutionary technology that has transformed the field of natural language processing. This technique allows each word (token) in a sentence to form direct relationships with all other words.

Working Principles

1. Tokenization Stage: Input text is divided into individual tokens.
2. Attention Application: Each token calculates its relevance to all other tokens.
3. Mathematical Implementation:
   • Each token is converted into Query, Key, and Value vectors.
   • The relevance between a specific token (Query) and other tokens (Keys) is calculated.
   • Weights are applied to the Values based on the calculated relevance.
   • This is expressed as the ‘sum of Value * Weight’.

Multi-Head Attention

• Definition: A method that calculates multiple attention vectors for a single token in parallel.
• Characteristics: Each head (styles A, B, C) captures token relationships from different perspectives.
• Advantage: Can simultaneously extract various information such as grammatical relationships and semantic associations.

Key Benefits

1. Contextual Understanding: Enables understanding of word meanings based on context.
2. Long-Distance Dependency Resolution: Can directly connect words that are far apart in a sentence.
3. Parallel Processing: High computational efficiency due to simultaneous processing of all tokens.

Applications

Transformer-based models demonstrate exceptional performance in various natural language processing tasks including machine translation, text generation, and question answering. They form the foundation of modern AI models such as GPT and BERT.

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