My guess is that the question translated to a medium/strong penalization for the word "the" and that's why it did so well at first, but as it generated more and more this original penalization got diluted by the new probabilities of the decoder (as "the" would have a high generation probability for obvious reasons). And since the decoder is greedy, it eventually wrote itself into a corner where "the" overcome whichever penalty the original prompt set.

Both GPT-3.5 and GPT-4 struggle with this sort of question, whether requiring a lipogram or counting words/sentences or using unusual quoting/syntax, which asks about fine-grained details of inputs which are usually irrelevant but which also don't seem to be related to my usual bugbear of BPE problems (because many of them are at the word or sentence level where tokenization should've become irrelevant) and which are bizarrely stubborn - you can point out exactly what the error is, and they still often won't fix it.

My theory is internal sparsity leading to irreversible blind spots & error: https://old.reddit.com/r/slatestarcodex/comments/1201v68/10w... The word 'the' is usually so irrelevant or easily predicted that it gets thrown away early on, been internally erased in favor of usually-more-important processing, so that by the time it even could realize that 'the' is important, it's too late to begin writing a correct answer, as opposed to a merely broadly good-sounding one.

And this happens regardless of where in the prompt it is because it happens as early as possible, so you can't use inner-monologue or even point out where the error is, because by the time it's processed your correction, the critical tokens have already been condensed too far - it literally cannot 'see' it anymore than someone telling you about something in your ocular blindspot helps you suddenly see it, by the time your consciousness understands, it's way too late.

I thought the failure was interesting too, enough to try on GPT-4. It succeeds with the same prompt.