When people talk about large language models (LLMs) like ChatGPT, GPT-4, or BERT, one word comes up over and over again: parameters. You’ll hear phrases like “GPT-3 has 175 billion parameters!” — but what exactly are these parameters? And why are they so important?
Let’s break it down in a simple, non-technical way.
🔧 What Are Parameters?
In the world of artificial intelligence, parameters are the tiny internal settings that a model learns from data. These settings help the AI figure out what to do — like how to predict the next word in a sentence, answer a question, or classify an image.
In a way, parameters are like knobs and dials that the model adjusts as it learns. Each knob slightly changes how the model behaves. By adjusting billions of them during training, the model slowly learns to "understand" language, patterns, or tasks.
You can think of parameters as the memory of the AI — not memory in the sense of storing facts, but memory in the sense of learned behavior.
🎯 Parameters = Weights + Biases
The term "parameters" usually includes two things:
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Weights – these control how much influence one piece of information has over another.
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Biases – these shift the output a little bit to help the model fine-tune its predictions.
Together, they make up the internal brain of the model. These are the things that get updated when a model is trained.
🧱 Traditional Neural Networks vs. Transformers
1. Traditional Neural Networks
In a basic neural network, like the kind used for image recognition or small-scale classification, parameters are mostly just weights and biases between layers of neurons.
Each "neuron" takes in inputs, multiplies them by weights, adds a bias, and passes the result forward. The network learns by adjusting these weights and biases during training, so it gets better over time.
These models might have thousands to millions of parameters.
2. Transformer-Based Models (like GPT)
Transformers are a special kind of neural network designed for understanding sequences — like sentences in a language.
They have much more complex architecture, with different kinds of parameters:
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Embedding parameters – These convert words or tokens into numbers the model can process.
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Attention parameters – These help the model figure out which parts of the input are most important at each moment (for example, focusing on "cat" when predicting the next word in “The cat sat on the...”).
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Layer normalization parameters – These help stabilize training and improve performance.
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Feed-forward network parameters – These are similar to traditional neural networks and are used inside each layer of the transformer.
These models often have millions to billions of parameters. The more parameters, the more complex patterns the model can learn — but also the more data and computing power it needs.
📊 Why Do More Parameters Matter?
Think of parameters like brain power.
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A model with 1 million parameters might only be able to do simple tasks.
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A model with 100 billion parameters can understand complicated relationships, subtle meanings in language, and even solve logic puzzles.
However, more isn’t always better. A model with too many parameters can:
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Be very expensive to train and run.
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Overfit — meaning it memorizes training data instead of generalizing to new data.
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Be slower in production unless optimized (like GPT-4-turbo).
So it’s about balance — finding the right number of parameters for the job.
🧠 Do Parameters Have Names?
Not individually. When you hear "175 billion parameters," those are just 175 billion numbers stored in large arrays — no names, no labels, just pure math.
But developers do group them into named categories, especially in the code. For example:
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Embedding weights
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Attention weights
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Linear layer biases
These names help developers understand which part of the model each parameter belongs to, even though the parameters themselves are anonymous.
🔍 How Are Parameters Trained?
During training, the model is shown tons of examples (like text from books, websites, and articles).
It makes predictions, compares them to the correct answers, and adjusts its parameters slightly to improve. This happens millions of times until the model becomes very good at language-related tasks.
This process is called gradient descent — a technique used to find the best combination of parameter values.
📌 Final Thoughts
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Parameters are the building blocks of intelligence in AI models.
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They are numbers that get adjusted during training to help the model learn.
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In transformer-based models (like GPT), parameters are more complex and specialized, but the idea is the same.
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The more parameters a model has, the more it can learn — but also the more computing power it requires.
So next time you hear about a model with "billions of parameters," you’ll know: that’s just another way of saying it has a massive, finely-tuned brain built from numbers.
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