Understanding Self-Attention and Multi-Head Attention in Large Language Models
Large Language Models (LLMs) such as GPT, Claude, and Gemini can generate remarkably coherent text, answer questions, summarize documents, and even write code. But underneath all of this is a deceptively simple idea:
An LLM predicts what token should come next.
To understand how LLMs make those predictions so effectively, we need to understand one of the most important ideas behind modern language models: attention.
This article takes you from the basics of LLMs to Self-Attention and finally to Multi-Head Attention.
1. First: What Does an LLM Actually Do?
At its core, an LLM predicts the next token based on the tokens that came before it.
A token can be:
A complete word
A part of a word
A single character
Sometimes punctuation or another small piece of text
For example, the sentence:
"The engineer fixed the server."
might be broken into several tokens rather than exactly one token per word.
When generating text, the model might predict:
The → engineer → fixed → the → server → ...
The model doesn't necessarily generate only one word at a time in the way humans think about words. It predicts tokens, and those predictions can continue for hundreds or even thousands of tokens, producing entire paragraphs or documents.
Why are LLMs so much better than older language models?
Modern LLMs have two major advantages:
1. Far more parameters
Parameters are the learned values inside the neural network. Modern LLMs contain vastly more parameters than traditional recurrent models.
2. Far more context
LLMs can consider relationships between many tokens in their context rather than treating language as a short sequence of immediately preceding words.
And this second capability is where Transformers and attention become extremely important.
2. The Transformer: The Architecture Behind Modern LLMs
The Transformer is the architecture that made today's generation of LLMs possible.
A traditional Transformer consists of two major components:
Input Text
│
▼
┌─────────────┐
│ Encoder │
└─────────────┘
│
▼
Intermediate Representation
│
▼
┌─────────────┐
│ Decoder │
└─────────────┘
│
▼
Output Text
Encoder
The encoder takes the input text and converts it into an internal representation that captures information about the text.
Decoder
The decoder uses that representation to generate useful output text.
A classic example is machine translation.
Suppose we want to translate:
"The engineer fixed the server."
from English into French.
The encoder processes the English sentence and creates an internal representation.
The decoder then uses that representation to generate the French translation.
3. But How Does the Transformer Understand Context?
This is where the key concept appears:
Self-Attention
Self-attention allows the model to examine the tokens in a sequence and determine which tokens are important to each other when understanding a particular token.
Consider this sentence:
"The animal didn't cross the road because it was tired."
What does "it" refer to?
Most humans immediately understand that "it" probably refers to the animal, not the road.
But the model has to learn this relationship mathematically.
Self-attention gives it a mechanism for doing exactly that.
Instead of treating every token independently, the model asks:
"For this token, which other tokens should I pay attention to?"
4. How Does Self-Attention Work?
For every token, the Transformer creates three representations:
Query (Q)
"What information am I looking for?"
Key (K)
"What information do I contain?"
Value (V)
"What information should I provide if I'm relevant?"
You can think of this like searching a database.
The Query is your search request.
The Key describes what each item contains.
The Value is the actual information you retrieve.
The model compares a token's Query with the Keys of other tokens to determine how relevant those tokens are.
Conceptually:
Query
│
├── compare with Key 1 → relevance
├── compare with Key 2 → relevance
├── compare with Key 3 → relevance
└── compare with Key 4 → relevance
These relevance scores become attention weights.
The model then uses those weights to determine how much information to take from each Value.
5. A Simple Example
Consider:
"The engineer fixed the server because it was crashing."
When the model processes the token "it", it needs to understand what "it" refers to.
Self-attention might produce something conceptually like:
"it"
│
┌──────────┼──────────┐
↓ ↓ ↓
engineer server crashing
0.15 0.55 0.20
These numbers are only illustrative, but they show the basic idea.
The model gives greater attention to "server" than to "engineer" when interpreting "it".
The important point is:
Self-attention does not mean looking at everything equally.
Instead, the model calculates different attention weights for different tokens.
Some tokens are highly relevant.
Others are less relevant.
6. Why Is This So Powerful?
Consider how humans understand a sentence.
When we read:
"The engineer fixed the server because it was crashing."
we don't interpret every word in isolation.
We connect:
engineer → who performed the action
fixed → what action happened
server → what was fixed
crashing → why the action was necessary
it → what the pronoun refers to
The meaning of one word can depend heavily on other words.
Self-attention allows the model to build representations where the meaning of a token is influenced by its surrounding context.
This is one of the major reasons Transformers are so effective at understanding language.
7. But One Attention Mechanism Isn't Enough
Now consider a more complicated sentence:
"The engineer gave the manager the report because he had requested it."
There are several relationships that the model needs to understand.
For example:
engineer ↔ gave
manager ↔ requested
report ↔ it
engineer ↔ he
These relationships are not all the same.
Some involve:
Grammar
Pronoun resolution
Meaning
Actions
Objects
Relationships between entities
A single attention mechanism might struggle to capture all of these different relationships effectively.
So the Transformer uses a more powerful idea:
Multi-Head Attention
8. What Is Multi-Head Attention?
The simplest way to understand Multi-Head Attention is to imagine multiple specialists reading the same sentence simultaneously.
Instead of having one attention mechanism, the Transformer has multiple attention heads.
Conceptually:
Sentence
│
┌────────────┼────────────┐
↓ ↓ ↓
Head 1 Head 2 Head 3
│ │ │
Grammar Meaning Context
relations relations relations
│ │ │
└────────────┼────────────┘
↓
Combine Results
│
↓
Final Representation
Each head can learn to focus on different types of relationships.
9. Why Do We Need Multiple Heads?
Imagine you're reading a paragraph.
At the same time, your brain might pay attention to several different things.
Person
Who is performing the action?
Object
What is the action being performed on?
Grammar
What does this pronoun refer to?
Time
When did something happen?
Meaning
What does the sentence actually mean?
Relationships
How are the different concepts connected?
A single perspective isn't necessarily enough to capture all of these relationships.
Multi-head attention allows the neural network to learn different patterns in parallel.
One attention head might become useful for grammatical relationships.
Another might focus on relationships between entities.
Another might capture positional or contextual relationships.
The important point is that the model learns what each head should specialize in during training. We shouldn't assume that every individual head literally corresponds to a human-defined category such as "grammar" or "meaning."
10. Self-Attention vs. Multi-Head Attention
This distinction is worth remembering.
Self-Attention asks:
"Which other tokens should I pay attention to?"
It calculates relationships between tokens within the sequence.
Multi-Head Attention asks:
"Can we examine those relationships from multiple learned perspectives at the same time?"
Multiple attention heads perform attention operations in parallel, allowing the model to capture different patterns in the same input.
11. Putting It All Together
Let's return to our example:
"The engineer gave the manager the report because he had requested it."
A Transformer doesn't simply read this sentence as a list of independent words.
Instead, attention mechanisms allow it to build contextual representations.
Conceptually:
Sentence
│
▼
Tokenization
│
▼
Token Representations
│
▼
┌─────────────────────┐
│ Multi-Head Attention│
└─────────────────────┘
│
┌─────────┼─────────┐
↓ ↓ ↓
Head 1 Head 2 Head 3 ...
│ │ │
└─────────┼─────────┘
↓
Contextual Representation
│
▼
Further Transformer
Processing
│
▼
Next-token prediction
The model repeatedly performs these kinds of transformations as information moves through the Transformer.
Eventually, the resulting representation is used to predict what token should come next.
And that simple prediction process, repeated over and over, is what allows an LLM to generate remarkably sophisticated text.
12. The Big Picture
If you're learning LLMs, it's useful to keep the hierarchy in your head:
LLM
│
└── Transformer
│
├── Attention
│ │
│ └── Self-Attention
│ │
│ ├── Query
│ ├── Key
│ └── Value
│
└── Multi-Head Attention
│
├── Head 1
├── Head 2
├── Head 3
└── ...
The exact architecture of modern LLMs can be more nuanced than this simplified picture. For example, many popular generative LLMs are decoder-only Transformers rather than the original encoder-decoder Transformer architecture.
But the core intuition remains extremely useful:
Tokens provide the pieces.
Self-attention determines which pieces are relevant to one another.
Multi-head attention examines those relationships from multiple learned perspectives.
The Transformer processes this contextual information.
The LLM uses the resulting representation to predict the next token.
And by repeating that process at enormous scale, an LLM can transform a simple sequence of token predictions into something that looks remarkably like understanding.
The One-Minute Takeaway
If you remember only four things, remember these:
1. LLMs predict tokens.
They generate text by repeatedly predicting what token should come next.
2. Transformers provide the architecture.
They allow models to process relationships between tokens efficiently.
3. Self-attention provides context.
It answers: "Which other tokens are important when understanding this token?"
4. Multi-head attention provides multiple perspectives.
It allows the model to learn different relationships between tokens simultaneously.
Self-Attention: "Who should I pay attention to?"
Multi-Head Attention: "Let's ask that question from multiple learned perspectives."
That is one of the fundamental ideas behind modern Large Language Models.
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