5 ms·
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 bas
by Joel_Mckay 18d 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 18d agoOK, I still have no idea what you actually think the "correct" answer is.
- quietbritishjim 18d agoI 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 18d agoAll 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