I was about to give it some credit for being thorough, even if overly so. But then I noticed these gems: thinking about the phonetics, "strawberry" has thre

I was about to give it some credit for being thorough, even if overly so. But then I noticed these gems: thinking about the phonetics, "strawberry" has three /r/ sounds... For example, in Spanish, it's "fresa," which has no "r"s, or in French, "fraise," which also has no "R"s... But to be absolutely certain, let's count them one by one: s-t-r-a-w-b-e-r-r-y. Okay, the first "r" is after the "a," then after the "b," and finally twice at the end before the "y." Wait, is that two or three "r"s at the end? Let's see: s-t-r-a-w-b-e-r-r-y. So, r-a-w-b-e-r-r-y. Okay, there are two "r"s before the "y." So, that's one after "a," one after "b," and two before "y," making it four "r"s in total. Wait a minute, now I'm confusing myself... For example, "copyright" has two "r"s, "arroba" has one "r,"... Wait, perhaps I'm confusing it with "arrowroot," which has two "r"s. Alternatively, maybe thinking of a word like "corroborate," which has four "r"s... and Alternatively, thinking about Middle English "strawberie," still three "r"s. It's not being thorough. It's just trained to sound like it's being thorough. It's producing the same garbage as any other LLM, this one just happens to get the answer right because it's been trained on data that includes discussions of the strawberry problem. There's certainly a unique mechanism which might make it better at searching a space for answers, and that's pretty neat in its own way.