> the kind of analysis the program is able to do is past the point where technology looks like magic. I don’t know how you get here from “predict the next word.” You're implicitly assuming that what you asked the LLM to do is unrepresented in the training data. That assumption is usually faulty - very few of the ideas and concepts we come up with in our everyday lives are truly new. All that being said, the refine.in…
>You're implicitly assuming that what you asked the LLM to do is unrepresented in the training data. This is just as stuck in a moment in time as "they only do next word prediction" What does this even mean anymore? Are we supposed to believe that a review of this paper that wasn't written when that model (It's putatively not an "LLM", but IDK enough about it to be pushy there) was trained? Does that even make sense?…
Similarly, if there are millions of academic papers and thousands of peer reviews in the training data, a review of this exact paper doesn't need to be in there for the LLM to write something convincing. (I say "convincing" rather than "correct" since, the author himself admits that he doesn't agree with all the LLM's comments.)
I tend to recommend people learn these things from first principles (e.g. build a small neural network, explore deep learning, build a language model) to gain a better intuition. There's really no "magic" at work here.