It's quite easy to invent a sentence that's never been uttered before, it's also fairly easy to hit the limits of the internet's knowledge in pretty much any complex discipline that's not software engineering.
And it's apparent when you speak to ChatGPT, he will hallucinate a lot to compensate for niche topics it doesn't know much about.
Occam's razor wouldn't point to ChatGPT having omniscient knowledge of all things ever thought of and that will ever be invented.
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.
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.
I don't think that blog post's excercise is a particularly useful demonstration of anything.
How can you possibly know what's in its training set? Its training set is so large that any "guess" about what is or is not in it is a fool's errand. Given that we know how the technology works, there's no reason to claim emergent behavior when Occam's razor would say the items are likely in the training set.