Generative AI : Encoder-Decoder

It is a neural network model that consists of two main parts:

1. Encoder: Processes and understands the input.
2. Decoder: Uses this understanding to generate meaningful output.

This model is commonly used in language processing tasks.

Photo by Hitesh Choudhary on Unsplash

Example: Language Translation

If we want to translate “Hello, how are you?” into Spanish:

1. The encoder converts the English words into a numerical representation (context vector).
2. The decoder takes this representation and generates the Spanish translation “Hola, ¿cómo estás?”.

How Does It Work?

Encoder:

• Takes a sequence of words (tokens) as input.
• Converts them into a fixed-size vector representation.
• This vector captures the context and meaning of the sentence.

Decoder:

• Takes the encoded representation as input.
• Generates words one at a time (auto-regressively).
• Uses previous words to predict the next word.

Stacking Encoders and Decoders:

• Transformers use multiple encoders and decoders stacked together.
• Each layer focuses on different parts of the input, improving accuracy.

🛠️ Why Use an Encoder-Decoder?

• Natural language has many words with similar meanings (e.g., “king” and “queen”).
• Instead of treating every word separately, the encoder groups similar words together.
• This helps reduce the model’s complexity and improves learning.

Example: Word Similarity

Consider the phrases:

• “Once upon a time, there lived a king.”
• “Once upon a time, there lived a queen.”