101–110 of 257 posts
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I won't defend the term but am curious what you think would have been also concise but more accurate. Calling them for example "inevitable statistical misdirections" doesn't really roll off the tongue.
It's a bug. Any other system where you put in one input and expect a certain output and get something else it'd be called a bug. Making up new terms for AI doesn't help.
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That's a good point. But re: not anthropomorphizing, what's wrong with errors, mistakes or inaccuracies? That's something everybody is familiar with and is more accurate. I'd guess most people have never actually experienced a hallucination anyway, so we're appealing to some vague notion of what that is.
> I'd guess most people have never actually experienced a hallucination anyway I actually think most people have. Every time you look at a hot road and see water that mirage is a form of hallucination.
> While the hallucination problem in LLMs is inevitable [0], they can be significantly reduced... Every article on hallucinations needs to start with this fact until we've hammered that into every "AI Engineer"'s head. Hallucinations are not a bug—they're not a different mode of operation, they're not a logic error. They're not even really a distinct kind of output. What they are is a value judgement we assign to the…
Oh, please. That's the same old computability argument used to claim that program verification is impossible.
Computability isn't the problem. LLMs are forced to a reply, regardless of the quality of the reply. If "Confidence level is too low for a reply" is an option, the argument in that paper becomes invalid.
The trouble is that we don't know how to get a confidence metric out of an LLM. This is the underlying problem behind hallucinations. As I've said before, if somebody doesn't crack that problem soon, the AI industry is overvalued.
Alibaba's QwQ [1] supposedly is better at reporting when it doesn't know something. Comments on that?
This article is really an ad for Kapa, which seems to offer managed AI as a service, or something like that. They hang various checkers and accessories on an LLM to try to catch bogus outputs. That's a patch, not a fix.
[1] https://techcrunch.com/2024/11/27/alibaba-releases-an-open-c...
Completely misses the fact that a big part of the reason why llms hallucinate sp much is because there's a huge innate bias towards producing more tokens over just stopping.
The less tokens produced at inference the lower the quality of the response will be. The process of thinking for an LLM involves the use of words, which is why prompts that ask the LLM to only return the answer will cause lower quality.
> While the hallucination problem in LLMs is inevitable [0], they can be significantly reduced... Every article on hallucinations needs to start with this fact until we've hammered that into every "AI Engineer"'s head. Hallucinations are not a bug—they're not a different mode of operation, they're not a logic error. They're not even really a distinct kind of output. What they are is a value judgement we assign to the…
False. It is (in this context) outputting a partial before full processing. Adequate (further) processing removes that "inevitable". Current architectures are not "final".
Proper process: "It seems like that." // "Is it though?" // "Actually it isn't."
(Edit: already this post had to be corrected many times because of errors...)
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> The trouble is that we software engineers have spent so long working in an artificially deterministic world that we're not used to designing and evaluating probabilistic quality control systems for computer output. I think that's a mischaracterization and not really accurate. As a trade, we're familiar with probabilistic/non-deterministic components and how to approach them. You were closer when you used quotes aro…
> As a trade, we're familiar with probabilistic/non-deterministic components and how to approach them. Yes, users.
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Calling them hallucinations was a huge mistake.
I won't defend the term but am curious what you think would have been also concise but more accurate. Calling them for example "inevitable statistical misdirections" doesn't really roll off the tongue.
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LLMs outputs are no more "hallucinations" than my output would be if I were asked to judge a dressage competition.
There is no source of truth for dressage competition results, these are accepted as jury preference judgement. There are plenty of matters where there is such a source of truth, and LLMs don't know the difference.
There is no «source of [_simple_] truth» for complex things, but there are more (instead of less) objective complex evaluations.
Note that this is also valid for factual notions: e.g., "When were the Pyramids built?".
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At scale, you are doing the same thing with humans too. LLMs seem to have an error rate similar to humans for the majority of simple, boring tasks, if not even a bit better since they don't get distracted and start copying and pasting their previous answers. The difference with LLMs is they simply cannot (currently) do the most complex tasks that some humans can, and when they do produce erroneous output, the errors…
> At scale, you are doing the same thing with humans too. LLMs seem to have an error rate similar to humans for the majority of simple, boring tasks, if not even a bit better since they don't get distracted and start copying and pasting their previous answers. This is the big one missed by the frequent comments on here wondering whether LLMs are a fad, or claiming in their current state they cannot be used to replace…
For example, compilers didn't put programmers out of business in the 60s, it made programming more available to people with higher level languages.
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We can't expect end users to understand what "statistical estimators trained by curve fitting" means. That's why we use high level terms like hallucination. Because it's something everyone can understand even if it's not completely accurate.
That's a good point. But re: not anthropomorphizing, what's wrong with errors, mistakes or inaccuracies? That's something everybody is familiar with and is more accurate. I'd guess most people have never actually experienced a hallucination anyway, so we're appealing to some vague notion of what that is.
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