Encoder vs. Decoder Transformer: A Clear Comparison

This blog clearly explains the encoder and decoder structures within transformer models, addressing a common point of confusion for developers. It details the functionality of each component and its specific design purpose in natural language processing tasks.

Are you trying to understand how Transformer models process sequences? You might be curious about the roles of the encoder and decoder transformer. Understanding their functions is key to grasping these powerful architectures.

This blog clearly explains each component's distinct responsibilities. You'll learn how encoders process input and how decoders generate output. We'll also highlight their collaborative relationship within the Transformer framework. This breakdown offers valuable insights if your work involves NLP or sequence-to-sequence tasks.

The Original Transformer Architecture: A Two-Tower Design

The original transformer architecture introduced in the paper “Attention Is All You Need” (Vaswani et al., 2017) comprises two main blocks:

• Encoder block

• Decoder block

Encoder-decoder architecture processes input text and generates an output sentence, leveraging self-attention and multi-head attention to capture relationships between tokens.

Encoder: Compressing the Input Sequence

The encoder ingests the input sequence—a sentence or paragraph—and transforms it into a context-rich representation.

Key Components of the Encoder Block

1. Embedding layer: Converts each input token into an embedding vector

2. Positional encoding: Adds position-based information so the model understands token order

3. Self-attention mechanism: Computes relationships between all tokens in the input sequence

4. Multi-head attention: Captures multiple context types across parallel heads

5. Layer normalization and feed-forward layers: Improve gradient flow and model capacity

Example:

For the input sentence: “The cat sat on the mat.”

The encoder block processes each word to understand how “cat” relates to “sat” or “mat” through attention scores.

✅ The encoder’s function is to generate a deep representation of the entire sequence for the decoder to use.

Decoder: Generating Output One Token at a Time

The decoder uses the encoder’s output and previously generated words to create the next output token.

Key Components of the Decoder Block

1. Masked multi-head attention: Allows each position to only attend to previous tokens

2. Cross attention: Connects the encoder’s output with the decoder

3. Feed-forward layers and layer normalization

The causal mask ensures the model doesn’t peek ahead, making decoder-only and encoder-decoder setups suitable for text generation.

Comparing Encoder vs. Decoder Transformer Roles

Feature	Encoder	Decoder
Input	Input sequence	Previously generated tokens
Output	Contextual representation	Output sequence
Attention	Self attention only	Masked self attention, cross attention
Directionality	Bi-directional	Auto-regressive (left to right)
Use Cases	Text classification, sentiment analysis, BERT model	Text generation, machine translation

Variants in the Transformer Family

Encoder Only Models

Encoder-only models like BERT specialize in understanding text. They’re perfect for text classification, sentiment analysis, and question answering.

✅ They do not generate output sequences—they classify or embed input.

Decoder Only Models

Decoder-only models like GPT focus on generating text from one token at a time.

They excel in:

• Text generation

• Question answering

• Continuation of input text

✅ Decoder generates output based on previously generated tokens using causal masks and masked multi head attention.

Encoder Decoder Models

Encoder-decoder models like T5 and MarianMT are tailored for machine translation and sequence-to-sequence tasks.

They combine the strengths of both:

• The encoder reads and understands the input

• The decoder generates the output sentence

✅ The encoder decoder setup allows processing input sequences of any length and producing output sequences of different lengths.

Deep Dive into Self-Attention and Multi-Head Attention

The self-attention mechanism lets a token focus on relevant information from all other tokens in the same sequence.

Each multi-head attention layer computes this in parallel with different projection matrices, helping the model learn different relationships (e.g., syntax vs. semantics).

In decoder block, self-attention is masked to prevent future token access, while cross-attention attends to the encoder’s output.

Example Use Case: Machine Translation

Consider translating “The cat sat.” to French.

1. Encoder processes the input sentence

2. Decoder generates “Le chat s'est assis.” token by token

3. At each step, the decoder uses:

   • previous tokens like “Le” and “chat”

   • encoder’s output to retain context

Summary of Key Concepts

• Encoder processes the input sequence to produce a rich representation.

• Decoder generates the output sequence, guided by encoder’s output and previous tokens.

• Encoder-only models handle classification tasks, while decoder-only models are used for generation.

• Encoder-decoder models bridge the two for tasks like machine translation.

• Positional encoding and self-attention allow the model to understand order and relationships in text.

• Decoder block includes masked multi-head attention and cross-attention.

By understanding how each component of the transformer model works, you can build or fine-tune models for tasks ranging from text classification to full-scale natural language processing pipelines.