I think we're using aggregate statistics in two different ways. Technically speaking - the whole idea with a language model is that you're learning to generali

I think we're using aggregate statistics in two different ways. Technically speaking - the whole idea with a language model is that you're learning to generalize underlying patterns of text, not just memorize the input text. Otherwise language models would be very good at echoing back training data but fail miserably during validation. If we go back to the training sequence - it's trying to maximize the posterior given the conditional probabilities in the sequence: P(y1, y2, ... yn ) = P(y1) * P(y2|y1) ... P(yn|y1...yn-1) That probability is by definition an aggregate; it's the best fit given potentially competing inputs of the training set that all have the same input conditional chains. Where generative LLMs have a leg-up is because they have such a large parameter space, large context windows, and coherent sampling strategies. This helps them stay internally consistent with their response data. But at the end of the day what they are learning are still patterns. That's why they aren't able to link content back to the exact source of origin; parameters fuse inputs from different places into one hybrid. Seeding a generative chain with Einstein or someone down the street doesn't change the fact that what's next is some fused learning from a lot of different training set inputs.