My 2 cents (disclaimer: I am talking out of my ass) here is why GPTs actually suck at fluid knowledge retrievel (which is kinda their main usecase, with them being used as knowledge engines) - they've mentioned that if you train it on 'Tom Cruise was born July 3, 1962', it won't be able to answer the question "Who was born on July 3, 1962", if you don't feed it this piece of information. It can't really internally corellate the information it has learned, unless you train it to, probably via synthethic data, which is what OpenAI has probably done, and that's the information score SimpleQA tries to measure.
Probably what happened, is that in doing so, they had to scale either the model size or the training cost to untenable levels.
In my experience, LLMs really suck at fluid knowledge retrieval tasks, like book recommendation - I asked GPT4 to recommend me some SF novels with certain characteristics, and what it spat out was a mix of stuff that didn't really match, and stuff that was really reaching - when I asked the same question on Reddit, all the answers were relevant and on point - so I guess there's still something humans are good for.
Which is a shame, because I'm pretty sure relevant product recommendation is a many billion dollar business - after all that's what Google has built it's empire on.
Probably what happened, is that in doing so, they had to scale either the model size or the training cost to untenable levels.
In my experience, LLMs really suck at fluid knowledge retrieval tasks, like book recommendation - I asked GPT4 to recommend me some SF novels with certain characteristics, and what it spat out was a mix of stuff that didn't really match, and stuff that was really reaching - when I asked the same question on Reddit, all the answers were relevant and on point - so I guess there's still something humans are good for.
Which is a shame, because I'm pretty sure relevant product recommendation is a many billion dollar business - after all that's what Google has built it's empire on.