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What does "solving" the hallucination problem look like, though? Humans also hallucinate all the time - in fact, arguably, this is one of the core reasons for the existence of this website and specifically this CW roundup thread - and it is something we've "solved" through various mechanisms of checking and verifying and holding people accountable, with none of them getting anywhere near perfect. Now, human hallucinations are more well understood than LLM ones, making them easier to predict in some ways, but why couldn't we eventually get a handle on the types of hallucinations that LLMs tend to have, allowing us to create proper control mechanisms for them such that the rate of actual consequential errors becomes lower than those caused by human hallucinations? If we reach that point, then could we say that hallucinations have been "solved?" And if not, then what does it matter if it wasn't "solved?"
"What does solving the hallucination looks like?" is a very good question. A major component of the problem is defining the boundaries of what constitutes "an error" and then what constitutes an acceptable error rate. Only then can you begin to think about whether or not that standard has been met and the problem "solved".
Sumarily the answer to that question is going to look very different depending on the use case. The requirements of the average white-collar office-drone looking to translate a news article, are going to be very different from the requirements of a cyber-security professional at a financial institution, or an industrialist looking to automate portions of thier process.
When I'm giving my intake speach to interns and new hires I talk about "the 9 nines". That is that in order to have a minimally viable product we must meet or exceed the standards of "baseline human performance" with 99.9 999 999% reliability. Imagine a test with a billion questions where one additional incorrect answer means a failing grade.
In this context "Humans also hallucinate" is just not an excuse. Think about how many "visual operations" a person typically performs in the process of going about thier day. Ask yourself how many cars on your comute this afternoon, or words in this comment thread have you halucinated? A dozen? None? I you think you are sure, are you "9 nines" sure?
A lot of the current refinement and itteration work on generative machine learning models revolves around adding layers of checks to catch the most egregious errors (not unlike as with humans as you observed) and giving users the ability to "steer" them down one path or another. While this represents an improvement over the previous generation such solutions are difficult/expensive to scale and actively deleterious to autonomy. The thinking being that "a robot" that requires a full-time babysitter might as well be an employee. This is why you can't buy a self-driving car yet.
I'm not sure what "baseline human performance" means in practice, but regardless of what actual objective criterion that means, we just have to get the error rate to be under 1/10^9 to be effective as a product, right? I don't understand how that, or any other rate you might choose, couldn't be reached, in principle.
Unimportant aside: I don't think 1 mistake in a billion is reasonable for any human or any tool, but, again, I don't know exactly what you're talking where the rubber meets the road - do you have any examples of interns who fail this or are just on the threshold, where you calculated that they fail at 1.1/10^9 or 0.9/10^9, to better illustrate this concept? But regardless, the exact number is unimportant.
It's not meant to be an excuse. I'm actually not sure how many 9s sure I am that I didn't hallucinate anything in my commute today, and I'm not sure that anything in my life exceeds 9 nines certainty. I'm not sure what point this exercise is supposed to make, though. Could you explain how my not being 9 nines certain that I'm not hallucinating things like this very conversation (I'd guess I'm 3 or 4 nines sure at most?) affects the point about an LLM's ability to be useful as intelligent, semi-autonomous tools if we lower their error rate to be beneath that of a typical human serving a similar role?
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