In principle, the task of minimizing loss on predicting human text does not stop being improvable once a system is as smart as a human.

Judge as a conceptual/technical claim as of 2023-04-06. True: the evidence (ML theory or expert explanation) shows that loss on predicting human-generated text can in principle keep falling beyond human-level ability, for example because predicting text well needs more than the ability of the humans who wrote it, and the irreducible entropy floor sits below what human-level prediction reaches. Mostly true: this holds broadly, with minor caveats (e.g. diminishing returns, an entropy floor). Mixed: experts are seriously split, or the claim holds only in narrow senses. Mostly false: the evidence shows improvement past human level is very limited or mostly blocked by the entropy floor, though not ruled out. False: the evidence establishes that loss minimization on human text stops improving at roughly human-level capability.

Set before the research began.

1. E1 Language Models Are Better Than Humans at Next-token Prediction arXiv (ar5iv rendering)

   “In both experiments, we find humans to be consistently worse than relatively small language models like GPT-Neo-1.3B or GPT-2-large at next-token prediction.”

2. E2 Language Models Are Better Than Humans at Next-token Prediction arXiv (ar5iv rendering)

   “Additionally, predicting the next token on OpenWebText sometimes requires very broad knowledge that may require an amount of memory that may exceed human limits.”

3. E3 Why Yudkowsky Is Wrong And What He Does Can Be More Dangerous — LessWrong LessWrong Own record

   “Accurately predicting human text is much harder than just writing stuff on the internet. If GPT were to perfect the skill it is being trained on, it would have to be much smarter than a human!”

4. E4 Why Yudkowsky Is Wrong And What He Does Can Be More Dangerous — LessWrong LessWrong · 6 June 2023 Own record

   “It's impossible for them to exceed the bounds of human knowledge and expertise, as they're inherently limited by the information they've been exposed to.”

An independent experimental study, published in December 2022 before the claim date, found humans consistently worse than small language models such as GPT-2-large at next-token prediction (E1). This shows that loss on human text can be pushed below the human level. The same paper gives a reason: predicting the next token can require knowledge and memory beyond human limits (E2). None of the evidence measures where the entropy floor sits. E4 states a contrary view as an opinion without data in the quoted text, and the experimental evidence supports the claim broadly, with minor caveats.