The LLM is also trained to predict next token given a sequence of "real" tokens. This means that any case where the LLM predicts tokens that don't look like the

The LLM is also trained to predict next token given a sequence of "real" tokens. This means that any case where the LLM predicts tokens that don't look like the "real" token string is Out of Domain. Children learn in an iterative manner generating language, self-evaluating their own language, given validations from parents, and interactions with others. We've also cultivated a curriculum learning approach to make sure that students are always learning language. We really don't know how small of a corpus is required for an LLM to learn. Perhaps there is an optimal curriculum which could be generated that demonstrates progressively more complex language and knowledge tasks to keep gradients large throughout training. Or a larger LLM could generate new training examples based on the cross-entropy loss of a given batch.