AI & Technology How LLMs Actually Understand Language

A beginner- breakdown of Transformers, Attention Mechanisms and why they changed everything in AI

You have probably used Large Language Models like ChatGPT, Claude or Gemini and wondered. How does Large Language Model actually understand what you are saying? 

It does not just look up answers - Large Language Model reads your words grasps the meaning and writes back something. That is not magic. That is a technology called a "Large Language Model". Powered by an architecture called the Transformer.

In this post we will break down how Large Language Models understand language. 

From the problem with older AI systems to the breakthrough of Transformers to the attention mechanism that sits at the heart of it all. No heavy math. Clear real explanations.

01 The Problem with Old AI Models

Before 2017 the dominant way to build language, AI was using Recurrent Neural Networks. These models processed text word by word. 

Like reading a sentence one letter at a time and trying to remember everything from the beginning. The further you got into a sentence the worse the model got at remembering earlier context.

Imagine reading a 500-word paragraph and only being allowed to remember the 10 words. You would completely lose the subject by the time you reached the conclusion. That was the limitation of Recurrent Neural Networks. 

They had a memory problem, and they could not be run in parallel making them painfully slow to train.

Large Language Models are like reading the page at once and understanding how every sentence relates to every other one. 

Recurrent Neural Networks are like reading an one word at a time and only keeping the last sentence in your head.

02 Enter the Transformer (2017)

In 2017 a team of Google researchers published a paper called "Attention Is All You Need." It proposed a new architecture. The Transformer. That threw out recurrence entirely and replaced it with a mechanism called self-attention.

Of processing words one by one in sequence the Transformer processes the entire sentence at once. Every word looks at every word simultaneously and decides which ones are most relevant to understanding it.

This parallelism made training faster and allowed models to capture long-range relationships in text far more effectively.

The core innovation of the Transformer is that every token can directly attend to every other token regardless of how apart they are in the sentence.

This single shift is what made modern Large Language Models like GPT-4, Claude, Llama and Gemini possible. They all use the Transformer architecture at their core.

03 What is the Attention Mechanism?

The attention mechanism is the engine inside the Transformer. In terms it allows a Large Language Model to assign a score of importance to every word in a sentence relative to every other word. And use those scores to build a richer understanding of meaning.

Consider the word "bat" in two different sentences:

"The bat flew out of the cave at night."

"He swung the bat. Hit a home run."

An old model would assign "bat" a fixed meaning. Attention allows the Large Language Model to look at surrounding words like "flew," "cave," or "swung," "home run" and understand that the word means something different in each context. That is the power of attention. It gives words context-sensitive meaning.

04 Query, Key & Value. The Core Mechanism

Under the hood the attention mechanism works using three vectors for each word: Query, Key and Value. Think of it like a search engine:

Query is the word asking: "What information do I need to understand myself "

Key is every other word saying: "Here's what I contain. Am I relevant to your query?"

Value is the actual content passed back when a match is found. The useful information.

The Large Language Model computes a score between every Query, and every Key applies a SoftMax function to normalize these into probabilities and then uses those probabilities to pull the Values.

 The result is a representation of each word that is now informed by its full context.

05 Multi-Head Attention. Seeing Multiple Perspectives

One round of Query-Key-Value attention captures one type of relationship. Language is rich. A sentence has grammar, semantics, tone and subject-object relationships all at once. That is why Transformers use Multi-Head Attention.

Of running attention once the Large Language Model runs it multiple times in parallel. Each "head" looking at a different aspect of the text. One head might track structure another might capture long-range topic relationships, and another might handle coreference. The outputs of all heads are then combined into one unified representation.

It is like reading a paragraph with lenses at the same time. One for grammar, one for emotion one for logic. Multi-head attention lets the Large Language Model use all of them simultaneously.

06 How Large Language Models Generate Text

Now you know how Large Language Models understand text. How do they write it? The answer is surprisingly elegant: token prediction.

Given a prompt the Large Language Model processes all the tokens through Transformer layers. At the end it outputs a probability distribution over every next word in its vocabulary. 

The word with the probability is chosen, appended to the input and the process repeats. 

Token by token. Until the response is complete.

Modern Large Language Models like GPT-4 or Claude stack 32 to, over 100 Transformer layers on top of each other each refining the Large Language Models understanding deeper and deeper.

By the time your input has passed through all those layers the Large Language Model has a nuanced representation of what you asked. And can generate a coherent contextually accurate response.

→ Wrapping Up

Large Language Models are not magic. They are the result of an engineered architecture built around one powerful idea: let every word understand every other word at the same time. 

The Transformer made this possible. The attention mechanism made it intelligent. Next-token prediction turned it into a conversation partner.

The time you type a message to a Large Language Model, and it responds with something surprisingly insightful you will know exactly what is happening under the hood. Billions of attention scores computed in milliseconds weaving your words into meaning.