A parameter is a learned numerical value that helps a neural network transform inputs. Weights represent connection strength, while a bias term adjusts the combined result. section 1.1
Training repeatedly updates these values using data until the model performs well for its objective. Parameter count, commonly referring to learned weights, serves as a rough shorthand for model complexity and capability—not a guarantee of performance. section 1.1
A large language model
Large language models can contain more than one trillion parameters. Increasing network size and depth can improve performance when sufficient data is available, but scale remains connected to data and does not stand alone. section 1.1
