But still refuse to answer "How many strings does a bass play with in water?" correctly, perhaps the chat monitors in the third world data entry centers will manually patch the nonsense for a more rational answer someday. lol =3
This was the first time I'd heard that gotcha question. I just threw it at Opus 5:
None — a bass in water is a fish, and fish are notoriously bad at music.
The instrument version plays four strings as standard (five and six-string basses exist for players who want to go lower or higher), and it prefers to stay dry.
Indeed, giving a definite answer to an ambiguous nonsense question is still incorrect.
A fish can play with as many strings as it finds, but only one when on a hook. Yet this too is an incorrect answer, as it again ignores the ambiguity in the phrasing. =3
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
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.)
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
But still refuse to answer "How many strings does a bass play with in water?" correctly, perhaps the chat monitors in the third world data entry centers will manually patch the nonsense for a more rational answer someday. lol =3
This was the first time I'd heard that gotcha question. I just threw it at Opus 5:
Seems like a pretty good answer to me!Indeed, giving a definite answer to an ambiguous nonsense question is still incorrect.
A fish can play with as many strings as it finds, but only one when on a hook. Yet this too is an incorrect answer, as it again ignores the ambiguity in the phrasing. =3
This feels like an xkcd 169 situation, to be honest.
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_Sax...
https://en.wikipedia.org/wiki/Blind_men_and_an_elephant
OK, I still have no idea what you actually think the "correct" answer is.
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.)
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
How do you know the fish is playing? Is he happy, enjoying it? =3
I agree, part of the ambiguity is also unfairly projecting our own subjective experience onto hapless creatures. =3