Error correction is also intrinsic to all of computing and telecommunications, though. That’s a loose comparison but I hope we can make progress on this and get it to a manageable state, even if zero is impossible in principle. A lot of things in life only asymptotically approach zero and yet we live.
This is not error correction issue though. Error correction means taking known data and adding redundancy to it so that damaed pieces can be repaired. This makes the message longer.
An llm’s output does not contain error correction. It’s just the output. And it doesn’t contain any errors, mathematically speaking. The hallucination is the correct output. It is what the statistics it gathered from its training set determined is most likely. A “correct” llm output is indistinguishable from a “hallucination”, mathematically, and always will be. A hallucination is simply “some output that some human, somewhere, doesn’t like”, and that’s uncomputable. And outputs that people subjectively consider as “hallucinations” cannot be eliminated, because an llm is, fundamentally, a probabilistic algorithm. If you added error correction to an llm’s output all you’d be able to recover is the llm’s original output, “hallucinations” and all.
Tldr: “hallucinations” are a subjective thing. A Hallucination" is not an error that can be corrected after-the-fact, because it is not an error in the first place.
If anyone says “What if we make an AI which specifically catches these hallucinations and then-” I will personally take a flight and come to your house and slap you.
Well the problems to be solved aren’t necessarily the technical ones. Another way of “solving” the problems is to stop trying to use it in contexts where it’s limitations are more trouble than they are worth.
Here it is being tasked with and falling to accurately summarize news, which is ridiculous because those news articles come with summaries already, headlines.
So a fix may not mean fixing the summary, but just skipping the attempt as superfluous.
There are uses for the state of LLMs as they are, but hard to appreciate when it’s being crammed down our throats relentlessly at things we never needed them for and watch them screw things up.
the problems with (the current forms of generative) AI will not be solved, because they cannot be solved. They are intrinsic to the whole framework.
Error correction is also intrinsic to all of computing and telecommunications, though. That’s a loose comparison but I hope we can make progress on this and get it to a manageable state, even if zero is impossible in principle. A lot of things in life only asymptotically approach zero and yet we live.
This is not error correction issue though. Error correction means taking known data and adding redundancy to it so that damaed pieces can be repaired. This makes the message longer.
An llm’s output does not contain error correction. It’s just the output. And it doesn’t contain any errors, mathematically speaking. The hallucination is the correct output. It is what the statistics it gathered from its training set determined is most likely. A “correct” llm output is indistinguishable from a “hallucination”, mathematically, and always will be. A hallucination is simply “some output that some human, somewhere, doesn’t like”, and that’s uncomputable. And outputs that people subjectively consider as “hallucinations” cannot be eliminated, because an llm is, fundamentally, a probabilistic algorithm. If you added error correction to an llm’s output all you’d be able to recover is the llm’s original output, “hallucinations” and all.
Tldr: “hallucinations” are a subjective thing. A Hallucination" is not an error that can be corrected after-the-fact, because it is not an error in the first place.
If anyone says “What if we make an AI which specifically catches these hallucinations and then-” I will personally take a flight and come to your house and slap you.
all the advertised AI detection tools are just that. Happy slapping!
Well the problems to be solved aren’t necessarily the technical ones. Another way of “solving” the problems is to stop trying to use it in contexts where it’s limitations are more trouble than they are worth.
Here it is being tasked with and falling to accurately summarize news, which is ridiculous because those news articles come with summaries already, headlines.
So a fix may not mean fixing the summary, but just skipping the attempt as superfluous.
There are uses for the state of LLMs as they are, but hard to appreciate when it’s being crammed down our throats relentlessly at things we never needed them for and watch them screw things up.