Model Parameter

A model parameter is a numerical element of a machine learning or deep learning model that is automatically adjusted through training. These parameters define the relationship between input data and output results, and are the most critical internal component directly tied to model performance and prediction accuracy. In a neural network, for example, the "weights" and "biases" of each layer are the representative model parameters. They are updated repeatedly during the training process, progressively converging on their optimal values. Characteristics and roles of parameters: • Govern the computational rules for transforming input data into output • Optimized in the direction that minimizes the loss function based on training data • The performance of a trained model depends heavily on how effective the learned parameters are • The number of parameters (= model size) affects the model's expressive power and computational load For reference, GPT-3 has 175 billion parameters and GPT-4 is reported to have even more—parameter count is commonly used as an indicator of progress in large language models (LLMs). Note that model parameters are distinct from "hyperparameters" (e.g., learning rate, batch size, number of layers), which must be manually configured. Model parameters are adjusted automatically during training, whereas hyperparameters require human input. In short, parameters are the essential element that determines "how an AI model behaves"—they are the very substance of the model's "brain."