Unfortunately, we do not want LLMs to tell us "all sorts of things," we want them to tell us the truth, to give us the facts. Happy to read how this is the wrong way to use LLMs, but then please stop shoving them into every facet of our lives, because whenever we talk about real-life applications of this tech it somehow is "not the right fit".
> we want them to tell us the truth, to give us the facts.
That's just one use case out of many. We also want it to tell stories, make guesses, come up with ideas, speculate, rephrase, ... We sometimes want facts. And sometimes it's more efficient to say "give me facts" and verify the answer then to find the facts yourself.
I think the impact of LLMs is both overhyped underestimated. The overhyping is easy to see: people predicting mass unemployment, etc., when this technology reliably fails very simple cognitive tasks and has obvious limitations that scale will not solve.
However, I think we are underestimating the new workflows this tech will enable. It will take time to search the design space and find where the value lies, as well as time for users to adapt to a different way of using computers. Even in fields like law where correctness is mission-critical, I see a lot of potential. But not from the current batch of products that are promising to replace real analytical work with a stochastic parrot.
That's a round peg in a square hole. As ive seen them called elsewhere today, these "plausible text generators" can create a pseudo facsimile of reasoning, but they don't reason, and they don't fact check. Even when they use sources to build consensus, its more about volume than authoritativeness.
I was watching the show, 3 Body Problem, and there was a great scene where a guy tells a woman to double check another man’s work. Then goes to the man and tells him to triple check the woman’s work. MoE seems to work this way, but maybe we can leverage different models that have different randomness and maybe we can get to a more logical answer.
We have to start thinking about LLM hallucination differently. When it’s follows logic correctly and provides factual information, that is also a hallucination, but one that fits our flow of logic.
Sure, but if we label the text as “factually accurate” or “logically sound” (or “unsound”) etc., then we can presumably greatly increase the probability of producing text with targeted properties
What on Earth makes you think that training a model on all factual information is going to do a lick in terms of generating factual outputs?
At that point, clearly our only problem has been we've done it wrong all along by not training these things only on academic textbooks! That way we'll only probabilistically get true things out, right? /s
