[bonoboTP]

I don't think this is a fundamental limitation. If the LLM is trained (through RLHF or something else) to go on a chain-of-internal-monologue (which may take arbitrarily long) to figure out what it will answer, then this kind of adaptive amount of compute can be achieved.

[ralphist]

Sure, but then how good are Transformers at making connections between distant concepts? The brain seems way better at this, and it doesn't feel like conversation.

[bonoboTP]

One of the big hopes of this "train on all text in existence" (including academic works and everything else) is that this enables connections that no human could make before, since there probably a total of zero people who are specialists in medieval history and molecular biology and also music theory and quantum computational complexity and what have you. However current models seem not to do much of this in my experience. Typically they remain in the specialist "persona" that rarely brings up connections that would be known by other human specialists (whom it would otherwise have no issue impersonating). But I'd imagine this can also be improved somehow.

Think about how often narrowly specialized academics reinvent a bad version of something that already has an elaborately understood theory in another field. (a meme example is when a medical researcher working on insulin metabolism reinvented the trapezoid rule for integration - but of course many more mundane cases happen every day). It would be a great opportunity to avoid this by checking with an LLM. Arguably if that researcher fed his idea to an LLM as it exists today, the LLM would have recognized that this is just the trapezoid rule. In fact I also use it for such "sounding board" where it gives me good googleable terms like, "what you're looking for is called an XYZ".