these things don’t “understand”. Ask it something which is “to much OOD” and you get wrong answers, even when a human would give the correct answer according to the training set.
I said they mimic understanding well enough, that wasn’t a claim LLMs actually understand.
Sure training dataset limits apply,
And sure they very likely fail when the question is OOD, but figuring out the question is OOD isn’t that hard, so an honest “Sorry, your question is way too OOD” answer (instead of hallucinating) shouldn’t bee too difficult to implement.
these things don’t “understand”. Ask it something which is “to much OOD” and you get wrong answers, even when a human would give the correct answer according to the training set.
I said they mimic understanding well enough, that wasn’t a claim LLMs actually understand.
Sure training dataset limits apply,
And sure they very likely fail when the question is OOD, but figuring out the question is OOD isn’t that hard, so an honest “Sorry, your question is way too OOD” answer (instead of hallucinating) shouldn’t bee too difficult to implement.