Part 4/8:
The training can be imagined as adjusting various dials on an extensive machine, where the model's behavior is shaped entirely by numerous continuous values known as parameters or weights. Each model can possess hundreds of billions of these parameters, which no human explicitly sets. Instead, they start at random and are refined through an extensive learning process involving large sets of text.
The training method employs an algorithm known as backpropagation, which adjusts the parameters to enhance the model's accuracy. After being provided with a training example—irrespective of its length—the model predicts what the next word should be and is adjusted based on its accuracy. This iterative process leads to improved predictions on unseen text.