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Comment by crazygringo | Hacker News Reader

> LLMs don't "hallucinate" or "lie." They have no intent.

You're just arguing about semantics. It doesn't matter in any substantial way. Ultimately, we need a word to distinguish factual output from confidently asserted erroneous output. We use the word "hallucinate". If we used a different word, it wouldn't make any difference -- the observable difference remains the same. "Hallucinate" is the word that has emerged, and it is now by overwhelming consensus the correct word.

> Whenever they get something "right," it's literally by accident.

This is obviously false. A great deal of training goes into making sure they usually get things right. If an infinite number of monkeys on typewriters get something right, that's by accident. Not LLM's.

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While I agree that we need a word for this type of behavior, hallucinate is a wrong choice IMO.

Hallucinations are already associated with a type of behavior, which is (roughly defined) "subjectively seeing/hearing things which aren't there". This is an input-level error, not the right umbrella term for the majority of errors happening with LLMs, many if which are at output-level.

I don't know what would be a better term, but we should distinguish between different semantic errors, such as:

- confabulating, i.e., recalling distorted or misinterpreted memories;

- lying, i.e., intentionally misrepresenting an event or memory;

- bullshitting, i.e., presenting a version without regard for the truth or provenance; etc.

I'm sure someone already made a better taxonomy, and hallucination is OK for normal public discussions, but I'm not sure why the distinctions aren't made in supposedly more serious works.

I mean, I think you're right that confabulation is probably a more correct technical term, but we all use hallucinate now, so it doesn't really matter. It might have been useful to argue about it 4 or 5 years ago, but that ship has long since sailed. [1]

And I think we already distinguish between types of errors -- LLM's effectively don't lie, AFAIK, unless you're asking them to engage in role-play or something. They mostly either hallucinate/confabulate in terms of inventing knowledge they don't have, or they just make "mistakes" e.g. in arithmetic, or in attempting to copy large amounts of code verbatim.

And when you're interested in mistakes, you're generally interested in a specific category of mistakes, like arithmetic, or logic, or copying mistakes, and we refer to them as such -- arithmetic errors, logic errors, etc.

So I don't think hallucination is taking away from any kind of specificity. To the contrary, it is providing specificity, because we don't call arithmetic errors hallucinations. And we use the word hallucination precisely to distinguish it from these run-of-the-mill mistakes.

[1] https://trends.google.com/explore?q=hallucination&date=all&g...

> You're just arguing about semantics. It doesn't matter in any substantial way.

While I agree for many general aspects of LLMs, I do disagree in terms of some of the meta-terms used when describing LLM behavior. For example, the idea that AI has "bias" is problematic because neural networks literally have a variable called "bias", thus of course AI will always have "bias". Plus, a biases AI is literally the purpose behind classification algorithms.

But these terms, "bias" and "hallucinations", are co-opted to spin a narrative of no longer trusting AI.

How in the world did creating an overly confident chatbot completely 180 years of AI progress and sentiment?

Terminology sucks. There is an ML technique called "hallucinating", that can really improve results. It works, for example, on Alphafold, and allows you to reverse the function of Alphafold (instead of finding the fold that matches a given protein or protein complex, find a protein complex that has a specific shape, or fits on a specific shape).

It's called hallucination because it works by imagining you have the solution and then learning what the input needs to be to get that solution. Treat the input or the output as weights and learn an input that fits an output or vice-versa instead of the network. Fix what the network sees as the "real world" to match what "what you already knew", just like a hallucinating human does.

You can imagine how hard it is to find papers on this technique nowadays.

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