> As far as I can tell, it is correct: It does not know the contents of the paper. It barely understands the high-level concepts involved LLMs work by predicti

> As far as I can tell, it is correct: It does not know the contents of the paper. It barely understands the high-level concepts involved LLMs work by predicting the next bunch of words - it's advanced auto complete. If most of the training data replies "I don't" to the question 'Do you, or do you not know the contents of Dr. Franz 1994 paper "Code Generation on the Fly: A key to portable software"?' then that's what the LLM will say. It's half useful for answering frequently asked questions but don't expect it to evaluate its current state and give you an accurate answer. > We don't know enough about what "knowing" something or "processing" a concept means in terms of human thought processes to know whether there's a meaningful distinction between the level at which an LLM processes these concepts vs. humans or whether there is a meaningful distinction between reasoning and intelligence vs. "modelling the statistical properties of human languages". But we do know what LLMs do, model language. Not knowledge. Not “thought”. Language. And the way we get them to spit out output that’s satisfactory to us is just absurd. If you read the link I posted earlier, you will know that if you set the “temperature” of an LLM to zero, it just repeats itself talking in circles. It’s only by adding randomness to its “next token” search that we get output that possibly satisfactory.