Model parameters are adjusted during training to encode patterns found in data. In a neural network, parameters include weights and biases that influence how signals move through layers and how strongly one internal unit affects another.
Individual parameters are usually difficult to interpret in isolation because behavior emerges from many interacting values. Interpretability methods therefore often study groups of parameters, activation patterns, learned features, or circuits rather than assigning one meaning to one weight.
ELI5
A model parameter is one of the learned numerical values inside an AI model. Training adjusts huge collections of these values so the model can recognize patterns and turn new inputs into predictions or generated outputs.
For example, parameters in a language model help determine which token is likely to follow the supplied text. Having more parameters can increase size and memory demand, but architecture, data, training, and precision also strongly affect capability.
How does an AI model learn parameters?
Training repeatedly measures prediction error and uses an optimization algorithm to adjust parameters in directions that reduce that error.
Is one model parameter equal to one concept?
Usually not. Concepts and behaviors generally emerge from distributed interactions among many parameters and activation patterns.
