What Are Parameters in LLMs? A Simple Explanation
▼ Summary
– Parameters are the adjustable settings in a large language model that control its behavior, analogous to the paddles and bumpers in a vast pinball machine.
– The number of parameters has grown dramatically, from GPT-3’s 175 billion to newer models like Gemini 3, which may have trillions, though exact figures are now kept secret due to competition.
– Parameters are assigned values through an iterative training algorithm that adjusts them to reduce errors, a process repeated until the model performs as desired.
– Training an LLM is computationally immense, involving quadrillions of calculations as each of billions of parameters is updated thousands of times, requiring vast energy and specialized computers.
– Key parameter types include embeddings, weights, and biases, with embeddings being the mathematical representations of words or tokens that gain meaning during training.
Imagine a vast, intricate machine with billions of tiny dials and switches. Each adjustment changes how the machine operates, guiding its responses and shaping its understanding. This is essentially what parameters are within a large language model. They are the fundamental, learned components that dictate how the model processes information and generates text. While the scale is staggering, from hundreds of billions to potentially trillions of these numerical values, their core function remains consistent across different AI systems.
In simple terms, a parameter acts like a variable in a mathematical equation. Just as changing the value of ‘x’ in an equation alters the result, adjusting a model’s parameters changes its output. These values are not programmed by hand. Instead, they are discovered through an extensive automated training process. Initially, every parameter is set to a random number. The model is then fed massive amounts of text data. With each piece of information, the model makes predictions, checks for errors, and systematically updates its parameters to reduce those mistakes. This cycle repeats countless times, with the algorithm making minuscule tweaks across all parameters until the model’s performance meets the desired standard. The final, fixed set of parameters represents the model’s “knowledge” and operational rules.
This process is computationally monumental. For a model like GPT-3 with 175 billion parameters, each one might be adjusted tens of thousands of times during training. This results in quadrillions of calculations, requiring thousands of specialized computers running continuously for months and consuming vast amounts of energy. The sheer scale is what makes modern AI both powerful and resource-intensive.
So, what specific roles do these billions of parameters play? They are organized into three primary categories: embeddings, weights, and biases.
First, embeddings are crucial for giving words meaning to the machine. Before training begins, a model’s vocabulary is defined, consisting of hundreds of thousands of unique tokens (which can be whole words or parts of words). However, these tokens are just arbitrary symbols to the computer. During training, the model learns to assign each token a unique numerical representation, an embedding, that positions it in a complex mathematical space. Words with similar meanings or usage end up with similar embedding values, allowing the model to grasp semantic relationships. This transformation of language into a numerical form is the essential first step in all its operations.
(Source: Technology Review)
