These are tests which require reasoning in humans. A human can't reasonably be expected to pass Quant or Verbal tests without reasoning about the information given, but--and I can't stress this enough--large language models are not humans.

Here's a sample question from the GRE Verbal Reasoning test:

> Upon visiting the Middle East in 1850, Gustave Flaubert was so [blank] belly dancing that he wrote, in a letter to his mother, that the dancers alone made his trip worthwhile.

> (A) overwhelmed by

> (B) enamored by

> (C) taken aback by

> (D) beseeched by

> (E) flustered by

Whether that specific question was in the training corpus or not, there are enough words in the sentence to suggest a positive association, including, significantly, "worthwhile." That alone possibly serves to narrow the answers down to A or B, with a preference for B, because it's more likely that "worthwhile" and "letter to x mother" are associated with "enamored" in general English-language text.

Look, the whole point of these models is that its not easy or even possible to trace the path any given input takes on its way to output, but we know the principles used in development, so I think it's rather more of a burden to explain how the clearly-explained principles in LLMs result in something other than the obvious. The fact that the results are so overwhelming that we become enamored by them, well, I'm taken aback by the seeming accuracy of some of the responses, but I beseech you to remember the other responses in which these LLMs are dramatically off-base, as if flustered--if LLMs could ever be flustered.