A Parameter is a defining, intrinsic value of a machine learning model that is learned from the training data during the training process (e.g., Pre-training or Fine-Tuning). Parameters are the core knowledge-encoding components of a neural network. These values (often referred to as Weights and biases) are adjusted iteratively via optimization algorithms (like Gradient Descent) to minimize the model’s error and enable it to perform tasks like Prediction and classification.
Context: Relation to LLMs and Search
The scale of parameters is the primary factor defining the size, power, and computational requirements of a Large Language Model (LLM) and is central to Generative Engine Optimization (GEO).
- LLM Size: Modern foundational LLMs (based on the Transformer Architecture) are described by the number of parameters they contain, ranging from millions (small models) to hundreds of billions (e.g., GPT-3, Llama, Gemini). These parameters collectively store the model’s vast knowledge of Syntax and Semantics.
- The Role of Parameters: Every connection between the artificial neurons in the LLM’s Transformer Architecture has an associated parameter (a weight). When an input Vector Embedding is processed, it is multiplied by these weights to determine the strength and importance of the information being passed from one neuron to the next. The collective adjustment of these parameters allows the model to learn the complex patterns of human language (Pattern Recognition).
- Efficiency in GEO: Managing parameters is crucial for efficiency. Techniques like Parameter-Efficient Tuning (PEFT) are used to adapt LLMs for search and Retrieval-Augmented Generation (RAG) by only training a small fraction of new, task-specific parameters, freezing the vast majority of the original model’s parameters to save compute and memory.
Parameters vs. Hyperparameters
It is critical to distinguish parameters from hyperparameters:
| Feature | Model Parameters (Weights and Biases) | Hyperparameters |
|---|---|---|
| Source | Learned automatically from the training data. | Set manually by the human operator before training begins. |
| Adjustment | Adjusted iteratively by the optimization algorithm (Gradient Descent). | Kept constant during the training run. |
| Example | The weight value on the connection between two neurons. | Learning Rate, batch size, number of hidden layers, Context Window size. |
| Role | Define the model’s knowledge. | Define the model’s training process and architecture. |
The quality of the final model (its ability to generalize and make accurate Prediction) is a function of both the optimal set of learned parameters and the correctly chosen hyperparameters that guided the learning process.
Related Terms
- Weights: The most common type of model parameter.
- Parameter-Efficient Tuning (PEFT): Techniques focused on minimizing the number of parameters that need to be trained.
- Fine-Tuning: The process of adjusting the parameters of a pre-trained model for a specific task.