Transformer Encoder-Decoder Architecture Explanation

This image is a diagram that visually explains the encoder-decoder structure of the Transformer model.

Encoder Section (Top, Green)

Purpose: Process “questions” by converting input text into vectors

Processing Steps:

1. Tokenize input tokens and apply positional encoding
2. Capture relationships between tokens using multi-head attention
3. Extract meaning through feed-forward neural networks
4. Stabilize with layer normalization

Decoder Section (Bottom, Purple)

Purpose: Generate new stories from text

Processing Steps:

1. Apply positional encoding to output tokens
2. Masked Multi-Head Self-Attention (Key Difference)
   • Mask future tokens using “Only Next” approach
   • Constraint for sequential generation
3. Reference input information through encoder-decoder attention
4. Apply feed-forward neural networks and layer normalization

Key Features

• Encoder: Processes entire input at once to understand context
• Decoder: References only previous tokens to sequentially generate new tokens
• Attention Mechanism: Focuses on highly relevant parts for information processing

This is the core architecture used in various natural language processing tasks such as machine translation, text summarization, and question answering.

With Claude