[glenndebacker]

I've noticed that when using a language model for rephrasing text it also sometimes seem to miss important details because it clearly has no real understanding of the text.

It's not a problem when you are aware of it and with some follow up input you can get it mitigated, but often I see that people tend to take the first output of these systems at face value. People should be a bit more critical in that regards.

[Bumblonono]

I really don't wanna be nitpicky, but what do you mean by 'no real understanding of the text'?

How do you benchmark something or someone understanding text?

I'm asking because the magic of LLM is the meta level which basically creates a mathematical representation of meaning and most of the time, when i write with an LLM, it feels very understanding to me.

Missing details is shitty and annoying but i have talked to humans and plenty of them do the same thing but actually worse.

[xanderlewis]

If you ask it basic mathematical questions, it becomes quickly clear that the ‘understanding’ it seems to possess is a mirage. The illusion is shattered after a few prompts. To use your comparison with humans: if any human said such naive and utterly wrong things we’d assume they were either heavily intoxicated or just had no understanding of what they’re talking about and are simply bluffing.

I guess at best you can say these models have an ‘understanding’ of language, but their ability to waffle endlessly and eruditely about any well-known topic you can throw at it is just further evidence of this — not that it understands the content.

[Bumblonono]

So we officially accept that certain voting people, irrational people are constantly heavily intoxicated or had no understanding?

Interestingly enough, tx to a talk from a brain researcher i understood that there are two major brain modes we run: Either the i just observe and do things i observed (were it doesnt' matter that you are gay or black but still vote for trump) and the logical mind were you see a conflict in stuff like this.

LLMs are interestingly enough somewere inbetween.

[FeepingCreature]

Can you post examples?

[xanderlewis]

Sure. Something I tried the other day was asking questions about modular arithmetic — specifically, phrased in terms of quotient groups of the integers. Things like ‘how many homomorphisms are there between Z/12Z and Z/6Z?’. I was able to trip it up very easily with these sorts of questions, especially when it tries to ‘explain’ its answers and it says ridiculous (but superficially and momentarily plausible-looking) things like ‘the only solutions to the equation 12x = 0 (mod 12) in Z/12Z are {[0], [3], [6], [9]}, therefore…’.

You can also just quiz it on certain basic definitions. Ask it for examples of objects that don’t exist (graphs or categories with certain properties, etc.). Sometimes it’ll be adamant that its stated example works, but usually it will quickly apologise and admit to being wrong only to give almost exactly the same (broken) argument again.

Another thing you can try is concocting some question that isn’t even syntactically well-formed (i.e. fails even a type check) like ‘is it true that cyclic integer lattices are uniformly bounded below in the Riemann topology?’. I imagine that one is too far out to work, but when I’ve played around I’ve found many such absurd questions ChatGPT was only too happy to answer — with utter nonsense, of course. It’s interesting (and, I think, quite telling) that such systems are seemingly almost completely unable to decline to answer a question. And the reason is that there’s no difference between hallucination and non-hallucination. Internally, it’s exactly the same process. It either knows or doesn’t know — but it doesn’t know that it knows (or doesn’t).

LLMs basically only work on questions that are very similar to, or identical to, questions that have already been widely asked and answered online or in books… hence their lack of utility in mathematical research, or even in calculating one’s taxes, or whatever.

I could provide some more literal examples, but I’d have to go and try some and pick the ones that work, and even then they might not work on your end because of the pseudorandomness and the fact that the model keeps getting updated and patched. It’s better to just play around on your own based on the ideas I’ve given.

The moral is to use LLMs as a powerful way of finding information, but don’t trust anything it says. Use it to find better sources more quickly than you’d be able to via a search engine.

[FeepingCreature]

Well sure but ... I think the foundational problem here is just the "being unable to refuse to answer a request." The rest of the behavior you describe just follows from it.

If you for instance threatened to shoot a human if it refused a request or admitted it didn't know something, they might answer in a very similar fashion.