4 ms·

This feels like an xkcd 169 situation, to be honest.

• Joel_Mckay 12d ago
  It was actually a trivial allusion to a rather old poetic parable, and highlights a foundational flaw in LLM inference model statistical salience. If a LLM based chat bot does ever answer it correctly, than you know with a fair degree of certainty it was content moderators stepping into the chat. Have a wonderful day. =3 https://en.wikisource.org/wiki/The_Poems_of_John_Godfrey_Saxe/The_Blind_Men_and_the_Elephant https://en.wikisource.org/wiki/The_Poems_of_John_Godfrey_Sax... https://en.wikipedia.org/wiki/Blind_men_and_an_elephant https://en.wikipedia.org/wiki/Blind_men_and_an_elephant
  • aesthesia 11d ago
    OK, I still have no idea what you actually think the "correct" answer is.
    • quietbritishjim 11d ago
      I think they want the correct answer to be "your question doesn't really make sense, so I'm not going to answer it". (But I also think Opus's answer is better.)
      • Joel_Mckay 11d ago
        All the answers I've seen so far are assuredly not inaccurate (the elephants trunk is like a snake), but never fully correct (an elephant is not a snake). While the LLM spits out each ambiguous context search result, it never answers the actual query without a human cheaters help. =3 https://en.wikipedia.org/wiki/Pareidolia https://en.wikipedia.org/wiki/Pareidolia