Re:Large language models are having their Stable Diffusion m...
by Sunhold on Sunday March 12, 2023 @08:27AM
(#35119287)
That appears to show that it can't answer some obscure trivia question to the standards of the blog post author. Nothing to do with generalizing concepts.
Re:Large language models are having their Stable Diffusion m...
by vkou on Sunday March 12, 2023 @02:47PM
(#35124408)
It's not an obscure trivia question. It's a question that anyone with an understanding of the material in question should be able to answer, with something vastly better than a superficial 'the two books are similar, but, like, different and talk about different stuff'.
It immediately cuts to the heart of the matter - an LLM does not understand things. It does not know anything but which word follows another. It can't do anything but regurgitate things it has read in an incredibly lossy manner.
But it's a neat party trick that seems to fool a lot of people into thinking that there's something legitimately useful, here.
Re:Large language models are having their Stable Diffusion m...
by simonw on Sunday March 12, 2023 @01:48PM
(#35123469)
Large language models are not search engines, even though a lot of people seem determined to treat them as such.
I don't think that blog post's excercise is a particularly useful demonstration of anything.
https://acoup.blog/2023/02/17/collections-on-chatgpt/
> If you know nothing about either book, this answer almost sounds useful (it isn’t). Now this is a trivial research task; simply typing ‘the limits of empire review’ into Google and then clicking on the very first non-paywalled result (this review of the book by David Potter from 1990) and reading the first paragraph makes almost immediately clear the correct answer is that Isaac’s book is an intentional and explicit rebuttal of Luttwak’s book, or as Potter puts it, “Ben Isaac’s The Limits of Empire offers a new and formidable challenge to Luttwack.”
> A human being who understands the words and what they mean could immediately answer the question, but ChatGPT which doesn’t, cannot: it can only BS around the answer by describing both books and then lamely saying they “intersect in some ways.”
> The information ChatGPT needed was clearly in its training materials (or it wouldn’t have a description of either book to make a lossy copy of), but it lacks the capacity to understand that information as information (rather than as a statistically correlated sequence of words). Consequently it cannot draw the right conclusion and so talks around the question in a convincing, but erronous way.