So I understand how the classical ML classification/regression works. I can see how if you applied the same method to each word you can produce sentences. "Dog

So I understand how the classical ML classification/regression works. I can see how if you applied the same method to each word you can produce sentences. "Dogs are" -> "animals that..." Where Im confused is how this method would learn logic. I can imagine after seeing a certain amount of patterns "dogs are animals, "birds are animals", "cats are animals", it encodes a concept like "x are animals", which is connected to other concepts like "animals are y" How is this encoded in the model? Can we see the abstraction it has formed? "for every word I give you, reverse every alternating word". How does this fit into next word prediction? Its not just a matter of seeing what a reversal or alternating is, it has to actually compute these things. That cant be just predict the next word iteratively. I imagine its a system of different models, with a "master" model trained on directing prompts -> type of model to use.