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How Self-Attention Works in Large Language Models: Explained with Simple Examples
Imagine you read the following sentence:
The trophy didn’t fit in the suitcase because it was too big.
What does “it” refer to?
The trophy?
Or the suitcase?
Most of us answer that question almost instantly without consciously thinking about it. Somehow, our brain knows which earlier words are important.
Large Language Models face exactly the same challenge.
Before predicting the next token, the model first needs to figure out which earlier tokens matter the most. Not every word contributes equally to the meaning of a sentence. Some words carry the core meaning, while others provide little useful information.
This ability to identify the most relevant parts of a sentence is called self-attention, and it’s one of the key innovations that made modern language models so successful.
Instead of treating every earlier word equally, the model learns to focus on the ones that provide the most useful information.
In this article, we’ll build an intuitive understanding of self-attention without diving into heavy mathematics. By the end, you’ll understand why the Transformer architecture changed natural language processing forever.
Why Earlier Models StruggledBefore Transformers, many language models processed text one word after another.
Imagine someone reading a book while trying to remember every previous sentence. As the story becomes longer, remembering details from earlier chapters becomes increasingly difficult.
Earlier neural network architectures faced a similar challenge. Although techniques like LSTMs and GRUs improved long-term memory, they still struggled with capturing relationships between words that were far apart in a sentence.
Consider this sentence:
The animal didn’t cross the road because it was tired.
When we read the word it we immediately understand that it refers to the animal.
For a machine, making that connection isn’t obvious.
The model needs a way to look back at the sentence and decide which earlier words matter most.
That is precisely what self-attention was designed to do.
♦Image created by author using AIWhat Is Self-Attention?Imagine you’re attending a meeting with ten people.
At the end of the discussion, someone asks:
Who approved the budget?
You don’t replay every sentence spoken during the meeting.
Instead, your brain immediately focuses on the finance manager because that’s the person most likely to have the answer.
Your attention naturally filters out information that isn’t relevant.
Self-attention works in a similar way.
Instead of treating every token equally, the model asks:
Which previous tokens should I pay attention to before making my next prediction?
Every token examines the other tokens in the sentence and decides how much importance each one deserves.
Some receive a great deal of attention.
Others receive very little.
Every Token Looks at Every Other TokenAt first, this raises an interesting question.
If every token can look at every other token, how does the model know which word came first?
Self-attention alone doesn’t understand word order.
That’s why Transformers add positional encoding, which gives every token information about its position in the sentence before attention is calculated. Positional encoding and self-attention work together — one tells the model where a token appears, while the other helps decide which tokens matter most.
Without positional encoding, the sentence:
Dog bites man
would look almost identical to
Man bites dog.
Let’s look at a simple sentence.
The cat sat on the mat.
When processing the token sat, the model doesn’t only look at the previous word.
Instead, it considers every token in the sentence.
An example attention distribution might look like this:
| Token | Attention |
|-------|----------:|
| The | 5% |
| cat | 60% |
| sat | 20% |
| on | 10% |
| mat | 5% |
The model isn’t reading the sentence strictly from left to right.
Instead, it builds relationships between all tokens simultaneously.
This ability to connect distant words is one of the biggest reasons Transformers outperformed earlier language models.
♦Image created by author using AIUnderstanding Attention ScoresHow does the model decide which words deserve more attention?
It calculates an attention score between every pair of tokens.
A higher score means one token is more relevant to another.
Imagine the scores looked like this:
cat -> sat = 0.82
cat -> mat = 0.11
cat -> the = 0.07
These numbers aren’t manually programmed.
During training, the model gradually learns which relationships are useful for making accurate predictions.
After seeing billions of examples, it becomes remarkably good at identifying important connections.
Queries, Keys, and ValuesMost explanations of self-attention immediately introduce three unfamiliar terms:
- Query
- Key
- Value
That can make the idea seem much more complicated than it really is.
Instead, let’s first understand why these concepts exist.
A useful analogy is searching for a book in a library.
Imagine you’re looking for a book about machine learning.
- Your search request is the Query.
- Every book has a label describing its topic. That’s the Key.
- The contents inside the matching book are the Value.
You compare your search request with every book label.
The closer the match, the more likely you are to open that book.
Self-attention works almost the same way.
Each token creates:
- a Query describing what information it’s looking for
- a Key describing what information it offers
- a Value containing the information that will actually be shared
The model compares every Query with every Key.
The better the match, the more influence that token’s Value has on the final representation.
You can think of Query, Key, and Value as a search engine.
The Query represents what the token is looking for.
The Key describes what information another token offers.
The Value contains the information that can be shared.
Although the underlying mathematics involves matrix multiplication and dot products, the core idea is surprisingly intuitive:
Tokens that are more relevant to each other receive more attention.
♦Image created by author using AIMulti-Head AttentionLooking at language from a single perspective isn’t always enough. Language contains many different types of relationships.
Imagine asking a group of experts to analyze the same sentence.
One focuses on grammar.
Another looks for relationships between subjects and verbs.
Another tracks pronouns.
Another searches for long-distance connections.
Multi-head attention works in a surprisingly similar way.
The outputs from all attention heads are then combined before passing to the next Transformer layer.
♦Image created by author using AIWhy Self-Attention Changed AISelf-attention solved several problems that earlier language models struggled with.
It allows every token to interact directly with every other token in the sequence, making it much easier to capture long-range relationships.
Unlike recurrent models, Transformers can process all tokens in parallel during training, significantly improving training efficiency on modern hardware.
Because of these advantages, self-attention became the foundation of today’s Large Language Models.
Self-attention does have one important limitation.
Every token compares itself with every other token.
As the input becomes longer, every token has to compare itself with more tokens, causing the amount of computation to grow rapidly.
That’s one reason researchers continue developing more efficient attention mechanisms for long-context models.
Whether you’re using GPT, Claude, Gemini, Llama or DeepSeek, they all rely on this same fundamental idea.
Does Self-Attention Mean the Model Understands Language?Not exactly.
Self-attention helps the model decide which parts of the context deserve the most focus.
It does not mean the model understands language the way humans do.
The model still learns statistical patterns from enormous amounts of text.
Self-attention is simply one mechanism that makes those learned patterns far more useful.
Common MisconceptionsSelf-attention is memory.
Not quite. Attention determines which tokens are most relevant at the current moment.
Memory is a broader concept involving the model’s parameters and its context window.
Every token receives equal attention.
No. Different tokens receive different attention scores depending on the context.
Self-attention alone makes Large Language Models intelligent.
Self-attention is one important component. Embeddings, positional encoding, feed-forward networks, training data, optimization algorithms, and massive computational resources all contribute to the model’s capabilities.
Key Takeaways- Every token can attend to every other token.
- Attention scores are learned during training.
- Queries, Keys, and Values determine how attention works.
- Multi-head attention captures different types of relationships.
- Self-attention is one of the core ideas behind every Transformer model.
Self-attention solved one of the biggest challenges in language modeling:
How can a model decide which earlier words matter most?
Instead of reading text one token at a time and hoping important information isn’t forgotten, Transformers allow every token to examine the entire context before making a prediction.
That simple shift completely changed modern AI.
Today, every time ChatGPT generates a response, millions of attention calculations happen behind the scenes.
Before making a prediction, every token effectively asks:
Out of everything I’ve seen so far, what deserves my attention right now?
The answer to that question guides the model toward its next prediction.
Once you understand self-attention, ideas like Transformers, inference, context windows, and even AI agents become much easier to understand because they all build on this same foundation.
♦How Self-Attention Works in Large Language Models: Explained with Simple Examples was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.