Decoding how Large Language Model works: The Transformer Architecture (Part 1)
Hey There! Welcome back to my weekly newsletter! Today, we're diving into something truly fascinating and fundamental to the AI world we're seeing explode around us. Remember when Large Language Models (LLMs) like GPT-4 burst onto the scene? It felt like magic, didn't it? Everyone was blown away by their incredible abilities.
Today, we're going to pull back the curtain and look at how these amazing language models actually work. To make it easier to grasp, we're breaking this down into two parts.
In this first part, we'll explore the core technology that powers these LLMs. In our next newsletter, we'll delve into how these models have evolved over time to become so powerful.
The AI Revolution in Language: From Simple Beginnings to Human-Like Text
Think about the AI tools you might use daily, like ChatGPT etc. They can write emails, translate languages, and even craft creative stories with a remarkable human touch. It's easy to forget that these are complex artificial systems! This incredible leap in AI didn't just happen; it was built on a groundbreaking idea that changed everything.
Before the Big Bang: How AI Used to Understand Language
Before 2017, the main tools we used for AI to understand language were called Recurrent Neural Networks, or RNNs for short. These early models could do some basic things like generate simple sentences or translate short phrases. But they weren't very good at understanding the deeper meaning or context in longer pieces of text – kind of like a child learning to read one word at a time without grasping the whole story.
Then, everything changed. A team of smart people at Google published a research paper with a straightforward title: "Attention Is All You Need." This paper introduced the Transformer architecture – a completely new way of thinking about how AI could process language. This breakthrough is the engine that drives the most advanced AI language systems we see today.
Why the Old Way Struggled: The Limits of RNNs
To really appreciate how revolutionary Transformers were, let's understand why RNNs had trouble with more complex language.
Imagine you're reading a sentence like, "Because he was late for school, the student quickly ate his breakfast." To understand who "he" is, you need to remember the beginning of the sentence. Your brain easily connects "he" to "the student."
RNNs tried to do something similar. They processed language word by word, like reading a book aloud. As they read, they tried to keep a mental note (called a "hidden state") of what they'd seen before. Think of it like passing a note from one person to the next in a line. Each person adds a little bit of information to the note before passing it on.
But this word-by-word approach had a couple of big problems:
Losing the Thread: When dealing with longer pieces of text, the information from the beginning would often get lost or become fuzzy by the time the RNN got to the end. It was like the note getting crumpled and hard to read as it passed through many hands. This made it hard for the AI to understand the overall context.
Slow Processing: Because RNNs had to process one word at a time, they couldn't really work on multiple parts of the sentence at the same time. This made them slow and inefficient, especially when dealing with large amounts of text. It was like everyone in the line could only work on the note one after the other.
The Transformer's Big Idea: Paying Attention to What Matters
The Transformer architecture solved these problems with a brilliant new idea called Self-Attention. Instead of looking at words one by one, Transformers look at all the words in a sentence at the same time. Then, for each word, it figures out how important every other word is to its meaning.
Let's go back to our example: "Because he was late for school, the student quickly ate his breakfast." When the Transformer is processing the word "he," it can instantly see that "student" is the most important word to pay attention to in order to understand who "he" refers to. It doesn't have to rely on a fading memory of the words that came before.
Think of it like a group of people discussing something. Instead of each person whispering to the next, everyone can listen to everyone else at the same time and instantly understand how different parts of the conversation relate to each other.
This "paying attention" approach solved both the "losing the thread" problem and allowed the Transformer to process language much more efficiently.
Peeking Inside the Transformer: What's Under the Hood
The original Transformer has two main parts that work together:
Encoder: This part takes the input text (like a sentence) and turns it into a rich, detailed understanding of its meaning.
Decoder: This part takes the understanding from the encoder and uses it to generate new text (like a translation or an answer to a question).
Before the text even gets to the encoder, it goes through a few important steps:
Tokenization: The text is broken down into smaller pieces, usually words or even parts of words (like turning "running" into "run" and "ning"). These pieces are called "tokens."
Token Embedding: Each token is then converted into a list of numbers (a "vector"). These numbers represent the meaning of the token in a way that the AI can understand.
Positional Encoding: Because the Transformer processes all words at once, it loses the information about the order of the words in the sentence. Positional encoding adds this information back in, so the AI knows that "the cat sat" is different from "sat the cat."
The Decoder's Magic: Generating Text Like a Human
The decoder is the part of the Transformer that actually writes the text you see from models like ChatGPT. It does this through some clever tricks:
Looking Back, Not Forward (Masked Self-Attention): When the decoder is generating a sentence, it can only look at the words it has already written, not the words that are coming next. Imagine trying to finish a sentence without knowing what the last word will be – you have to rely on what you've already written to guide you. This forces the model to learn to generate text that makes sense step-by-step.
Connecting Input and Output (Encoder-Decoder Attention): The decoder also has a way to look back at the understanding created by the encoder. As it generates each new word, it can focus on the most relevant parts of the original input. This helps it stay on topic and generate text that is related to the input.
Building Text Word by Word (Autoregressive Process): The decoder generates text one token at a time in a loop. It looks at all the tokens it has generated so far, considers the input it received, and then predicts the most likely next token. It adds this new token to its output and repeats the process until it has generated a complete piece of text. Think of it like building with LEGO bricks – you start with a few bricks and keep adding more one by one until you have a complete structure.
From Transformers to GPT: The Rise of Modern AI Assistants
The GPT (Generative Pre-trained Transformer) family of models, like the famous ChatGPT, are built on the Transformer architecture. The key difference is that GPT models primarily use only the decoder part of the original Transformer design.
While the original Transformer was designed for tasks like translation (where you need both an encoder to understand the source language and a decoder to generate the target language), OpenAI's GPT models focused on generating text directly. They took the decoder and made some tweaks to make it incredibly good at creating all sorts of text, even without a separate encoder in the traditional sense.
How the Transformer Changed Everything: A Lasting Legacy
The Transformer architecture was a game-changer for natural language processing. By overcoming the limitations of older methods, it made it possible for AI to:
Understand and process much longer pieces of text while keeping the context.
Learn much faster and more efficiently from massive amounts of data.
Recognize complex connections between words, even if they are far apart in a sentence.
Scale up to incredibly large and powerful models.
This breakthrough continues to drive innovation in AI today. From Google's BERT to OpenAI's GPT and Meta's Llama, the core ideas from that "Attention Is All You Need" paper are the foundation of almost all the exciting AI language technologies we see around us.
In our next newsletter, we'll dive deeper into how GPT models specifically evolved from these fundamental principles to become the versatile AI assistants we use today. Stay tuned!