Earlier quoted context omitted.
> novel data, not present in dataset in a semantical sense This is your error, afaik. The idea of the architecture design / training data is to produce a space that spans the entirety of possible input, regardless of whether it was or wasn't in the training data. Or to put it another way, it should be possible to infer a lot of things about cats, trained on the entirety of human knowledge, even if you leave out every…
People seem to get really hung up on the fact that words have meaning to them, in regards to thinking about what an LLM is doing. It creates all sorts of illusions about the model having a semantic understanding of the training data or the interaction with the users. It's fascinating really how easily people suspend disbelief just because the model can produce output that is meaningful to them and semantically relate…
Or that one can construct a surprisingly intuitive black box out of a sufficiently large pile of correlations.
Because what is written language, if not an attempt to map ideas we all have in our heads into words? So inversely, should there not be a statistically-relevant echo of those ideas in all our words?