Transformer Architecture in Large Language Models (LLMs)

Introduction

Modern models like ChatGPT, Llama, Gemini, Claude and Mistral are based on a common architecture called Transformer Architecture.  Transformers are also know as the important architecture in the world of modern AI and NLP.  Mostly popular LLMs use this architecture.

Before the Transformers NLP system mainly were based on Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Encoder- Decoder Architecture. These model were capable of processing the sequential data but these models have some limitations.  These model process the words one by one, It is difficult to remember long context and difficulty to find relationship between distant words.

In 2017, Google researchers publish a famous research paper Attention Is All You Need".  This research paper gives the concept of Transformer Architecture for the first time.  Transformer completely removed the requirement of RNN and LSTM and used Attention Mechanism to understand the language.

With the help of attention Mechanism model become capable to find the relationship between the words in a sentence and can capture the context in better way.  So the Transformer Architecture was the revolutionary invention in the field of NLP.  Many powerful models are also using Transformer Architecture:

• ChatGPT
• LLaMA
• Gemini
• Claude
• BERT
• T5

Why Transformers Were Needed

Before Transformers, models relied on sequential processing.

Example:

I love learning Artificial Intelligence

An LSTM processes:

I
↓
love
↓
learning
↓
Artificial
↓
Intelligence

One word at a time.

Problems:

• Sequential Processing
• Limited Memory
• Slow Training
• Vanishing Gradient Problem

Researchers needed a model that could:

• Process words in parallel
• Understand long context
• Scale to huge datasets
• Train efficiently on GPUs

The solution was the Transformer.

High Level Transformer Flow

A Transformer processes text through multiple stages.

Input Text
      ↓
Tokenization
      ↓
Token Embeddings
      ↓
Positional Encoding
      ↓
Multi-Head Attention
      ↓
Add & Norm
      ↓
Feed Forward Network
      ↓
Add & Norm
      ↓
Output Representation

Every Transformer block follows this structure.

Step 1: Tokenization

Computers cannot understand raw text.

Example:

I love AI

Tokenizer converts text into tokens.

["I", "love", "AI"]

Then token IDs:

[12, 532, 981]

These IDs are simply integers but the model still does not understand their meaning.

Step 2: Token Embeddings

Token IDs are passed into the Embedding Layer.

Example:

12
↓
[0.52, -0.14, 0.89, ...]

Each token becomes a dense vector.

Example:

dog → [0.3, 0.9, ...]
cat → [0.31, 0.88, ...]
car → [-0.8, 0.2, ...]

Similar words become closer in vector space and embeddings provide semantic meaning to tokens.

Step 3: Positional Encoding

Transformers process all tokens simultaneously.

Problem:

Dog bites man

and

Man bites dog

contain the same words.  Without positional information, both sentences would appear identical so to solve this problem, positional information is added.

Final Embedding = Token Embedding + Positional Encoding

Now the model knows the position of every token.

Step 4: Multi-Head Attention

This is the heart of the Transformer.

Suppose we have:

The capital of France is Paris.

The word Paris is strongly related to:

France
capital

Attention identifies these relationships and Multi-Head Attention uses multiple attention mechanisms simultaneously.

Example:

Head 1 → Grammar
Head 2 → Semantic Meaning
Head 3 → Long Distance Dependency
Head 4 → Entity Relationships

Different heads learn different patterns which will gives a richer understanding of language.

Step 5: Residual Connections

As Transformers become deeper, training becomes difficult and Information may lost now to solve this problem, Residual Connections are used.

Flow:

Input
  ↓
Layer
  ↓
Output

Instead of only using:

Output

Transformer uses:

Output + Original Input

Benefits:

• Better gradient flow
• Stable training
• Deeper networks

Step 6: Layer Normalization

Neural network values can become unstable during training, some values can become very large and some become very small.  here the Layer Normalization stabilizes the activations.

Benefits:

• Faster convergence
• Stable gradients
• Better training performance

This is why Transformer blocks use:

Add & Norm

after major operations.

Step 7: Feed Forward Network (FFN)

Attention gathers information but gathered information must be processed. This is the job of FFN.

Simple flow:

Input
 ↓
Linear Layer
 ↓
Activation Function
 ↓
Linear Layer
 ↓
Output

Example:

Attention
↓
Collect Information

FFN
↓
Process Information

Complete Transformer Block

Now combine everything.

Input
  ↓
Multi-Head Attention
  ↓
Add & Norm
  ↓
Feed Forward Network
  ↓
Add & Norm
  ↓
Output

This is called:

Transformer Block

Modern LLMs contain many Transformer Blocks.