>because we can no longer find tasks that are feasible for normal humans but unsolved by AI.
"Answer "I don't know" if you don't know an answer to one of the questions"
"Answer "I don't know" if you don't know an answer to one of the questions"
It also seems oddly difficult for them to 'right-size' the length and depth of their answers based on prior context. I either have to give it a fixed length limit or put up with exhaustive answers.
However it is less true with info missing from the training data - ie. "I have a Diode marked UM16, what is the maximum current at 125C?"
https://chatgpt.com/share/698e992b-f44c-800b-a819-f899e83da2...
I don't see anything wrong with its reasoning. UM16 isn't explicitly mentioned in the data sheet, but the UM prefix is listed in the 'Device marking code' column. The model hedges its response accordingly ("If the marking is UM16 on an SMA/DO-214AC package...") and reads the graph in Fig. 1 correctly.
Of course, it took 18 minutes of crunching to get the answer, which seems a tad excessive.
It's very difficult to train for that. Of course you can include a Question+Answer pair in your training data for which the answer is "I don't know" but in that case where you have a ready question you might as well include the real answer anyways, or else you're just training your LLM to be less knowledgeable than the alternative. But then, if you never have the pattern of "I don't know" in the training data it also won't show up in results, so what should you do?
If you could predict the blind spots ahead of time you'd plug them up, either with knowledge or with "idk". But nobody can predict the blind spots perfectly, so instead they become the main hallucinations.
So there is nobody to know or not know… but there's lots of words.