Suppose 99.3% of answers to 'What is the airspeed velocity of an unladen swallow?" are "I don't know that." and the remainder are "11 m/s". What would the model answer?
When the LLM answers "I don't know.", this could be a hallucination just as easily as anything else.
I don't know :)
Actually though, I think the best response would be to say that the answer to the question isn't clear, but that 11 m/s is sometimes given as an estimate. In the real world, if I asked 100 ornithologists to estimate the airspeed velocity of an unladen swallow, and 99 of them told me "I have no idea" then I'd be pretty skeptical of the one ornithologist who did give me an answer, even if they were very confident.
"Eleven meters per second."
Full stop. It's humorous, and any reasonable interlocutor understands not to take it seriously.
Of course, there are more serious questions that demand more serious answers. LLMs will eventually need to be able to understand the current context and assess the appropriate level of confidence required in any answer.
And it's not uncommon that certain knowledge would be, well uncommon even among experts. Experts specialize.
Since the usefulness of ornithological examples is getting exhausted, let's say one out of a hundred lawyers works in bankruptcy. If you ask a million lawyers about the provisions of 11 USC § 1129 and only ten thousand know the answer, is the answer untrustworthy, just because bankruptcy lawyers are far rarer than civil and criminal lawyers?
My main worry about hallucinations is it means I absolutely can't rely on the output for anything important. If I ask what the safe dose for Tylenol for an infant is, the answer needs to be either correct or "I don't know". It's not acceptable for it to hallucinate 10x the safe dose.
Currently,we have models that make stuff up when they don't know the answer. On the other end, we'd have a model that's refuses to answer any question that's not common knowledge. It'll be safe (though it can never be completely safe), but essentially useless.
I suspect it'll be impossible to make a completely trustworthy and useful model unless it somehow has a concept of it's own knowledge. And can you have a concept of one's knowledge if you lack a concept of self?
>>> If they were trained with more uncertain content, perhaps they'd be better at expressing uncertainty as well.
>> (me) If you ask ChatGPT a question, and tell it to either respond with the answer or "I don't know", it will respond "I don't know" if you ask it whether you have a brother or not.
> This has nothing to do with thinking and everything to do with the fact that given that input the answer was the most probable output given the training data.
First of all, my claim was in response to "They cannot say 'I dont know'" and "perhaps they'd be better at expressing uncertainty".
ChatGPT can say "I don't know" if you ask it to.
Regarding whether LLMs are lookup tables, I responded to that in more detail elsewhere under this post:
This is why it is a fallacy to think an LLM contains anything other than the textual descriptions of our higher level thinking, and why LLM alone will only ever parrot intelligence.
Can you design a text only test that will differentiate between real intelligence and the parroted kind?
Maybe LLMs don't truly "understand" questions, but they're good at looking like they understand questions. If they were trained with more uncertain content, perhaps they'd be better at expressing uncertainty as well.