CS10 - Understanding The Decoder 🤖 (Part III)
Decoding the Encoder: A Deep Dive into Transformer Architecture
This article is the third (and last!) part of a three-part deep dive into one of the most revolutionary AI architectures of our time:
Transformers.
Here’s what’s coming your way:
✅ Week 1: Understanding the Transformers architecture → Link
✅ Week 2: Understanding The Encoder → Link
✅ Week 3: Understanding The Decoder → Today!
Understanding the Decoder - Part III
The decoder's primary role is to generate text sequences step by step, transforming encoded information into meaningful output.
Structurally, the decoder mirrors the encoder in many ways—it consists of multiple layers, each containing:
Two multi-headed attention mechanisms
A pointwise feed-forward layer
Residual connections and layer normalization after each sub-layer
However, there’s a key difference: while the encoder’s attention focuses only on the input sequence, the decoder’s attention is split into two distinct tasks:
Masked Self-Attention – Ensures that the decoder can only attend to previous tokens, preventing it from “cheating” by looking ahead.
Encoder-Decoder Attention – Allows the decoder to focus on relevant encoded information, guiding the text generation process.
The final step in the decoder is a linear layer, which acts as a classifier, followed by a softmax function to assign probabilities to possible next words.
The Transformer decoder operates autoregressively—meaning it generates one token at a time, starting with a special start token.
At each step, it takes into account previously generated tokens and encoder outputs to predict the next word.
This process repeats until it generates a special end token, signaling that the sequence is complete.
Through this step-by-step decoding process, the model crafts coherent, context-aware text.
And this is precisely… the foundation of AI-powered language generation.
Before starting, here you have the full-resolution cheatsheet 👇🏻