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> 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.

> 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.

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...

This is still not true.

"Whenever they get something "right," it's literally by accident." "the random word generator"

First of, the input is not random at all which allows the question how random the output is.

Second, it compresses data which has an impact on that data. Probably cleaning or adjustment which should reduce 'random' even more. It compresses data from us into concepts. A high level concept is more robust than 'random'.

Thinking or reasoning models are also finetuning the response by walking the hyperspace and basically collecting and strengthening data.

We as humans do very similiar things and no one is calling us just random word predictors...

And because of this, "hallucinations -- plausible but factually incorrect outputs" is an absolut accurate description of what an LLM does when it response with a low probability output.

Humans also do this often enough btw.

Please stop saying an LLM is just a random word predictor.

OK, it's a semi-random word predictor.

I'm lost on your comment.

Its like you have an agenda against LLMs by now marking them as 'semi-random' and undermining the complexity and the results we get from current LLMs.

LLMs work by “walking” “hyperspace” ? Don’t try to correct people if you don’t know what you’re talking about. LLMs are quite literally non-deterministic language models, and that’s all they are.

Yes hyperspace: "Hyperspace is a concept from physics and science fiction referring to a higher-dimensional space"

I prefer to write hyperspace instead of n-dimensional.

Feel free to explain to me why you think my description is wrong.

LLMs are not non-deterministic in their nature. We add noise/randomess into specific layers to make them more 'creative'/'engaging' instead of always getting the exact same response we get variations.

Your whole sentence doesn't even contain a real description of what a LLM is. You say 'LLM are random LLMs'.

I explained that in a different comment already, feel free to check them out.

"Hallucinate" is a term of art, and does not imply a philosophical commitment to whether LLMs have minds. "Confabulation" might be a more appropriate term.

What is indisputable is that LLMs, even though they are 'just' word generators, are remarkably good at generating factual statements and accurate answers to problems, yet also regrettably inclined to generating apparenly equally confident counterfactual statements and bogus answers. That's all that 'hallucination' means in this context.

If this work can be replicated, it may offer a way to greatly improve the signal-to-bullshit ratio of LLMs, and that will be both impressive and very useful if true.

I think you are reading too much into the title. "Neuron" is a totally valid way of refering to the perception unit in such models. It's got nothing to do with biology apart from the name.

It’s been more than 3 years and people still can’t understand this.

People don't want to understand this.

People have a strong financial incentive to not understand this. It’s subprime mortgages.

Three top level comments so far, and as far as I can tell each is entirely pointless yapping about semantics around 'hallucination'.

Who cares? I wonder if any of the commenters is qualified enough to understand the research at all. I am not.

Yup, three years of fighting on "correct" use of words. Maybe someday people will accept that LLMs do "hallucinate" even tho it's not the same "hallucinate" as for humans.

Somewhat on the subject: here's a neuroscientist reflecting on our failure to model a worm's brain, a "mere" 302 neurons (3 parts, this one is the first). https://ccli.substack.com/p/the-biggest-mystery-in-neuroscie...

Biological systems are hard.

You're reasoning at the wrong abstraction level. This will not get you very far.

We don't understand the brain. We fully understand what LLM are doing, humans built them. The idea we don't understand what LLMs are doing is magical. Magical is good for clicks and fundraising.

We know how we built the machines, but their complexity produces emergent behavior that we don't completely understand.

This isn’t settled science, by the way. There’s evidence that the “emergent” behaviors are just a mirage.

Maybe humans are just random word generators too? Just more sophisticated ones. But I think I read some pieces that we are not random word generators but something better. Forgot the word.

I'm tired of this pseudointellectual reductionist response. It's not "literally by accident" when they're trained to do something, as if we are not also machines that generate next actions based on learned neural weights and abstract (embedded) representations. Your issue is with semantics rather than content.

Obviously "hallucinate" and "lie" are metaphors. Get over it. These are still emergent structures that we have a lot to learn from by studying. But I suppose any attempt by researchers to do so should be disregarded because Person On The Internet has watched the 3blue1brown series on Neural Nets and knows better. We know the basic laws of physics, but spend lifetimes studying their emergent behaviors. This is really no different.

I just kind of wish the behavior for "hallucinations" just didn't have such confident language in the context... actual people will generally be relatively forthcoming at the edge of their knowledge or at least not show as much confidence. I know LLMs are a bit different, but that's about the best comparison I can come up with.

Of course they hallucinate because we are training on random mode. +Since you mentioned 3blue1brown there is an excellent video on ANN interpretation based on the works of famous researchers who attempt to provide plausible explanations about how these (transformers based) archs store and retrieve information. Randomness and stochasticity is literally the most basic components which allow all these billions of parameters to represent better embedding spaces almost hilbertian in nature and barely orthogonal as training progresses.

The "emergent structures" you are mentioning are just the outcome of randomness guided by "gradiently" descending to data landscapes. There is nothing to learn by studying these frankemonsters. All these experiments have been conducted in the past (decades past) multiple times but not at this scale.

We are still missing basic theorems, not stupid papers about which tech bro payed the highest electricity bill to "train" on extremely inefficient gaming hardware.