> What bothers me about the hot takes is the claim that “all models do is hallucinate.” That collapses the distinction entirely

That is a problem for "Open"AI because they want to sell their products, and because they want to claim that LLMs will scale to superintelligence. Not for others.

"Bad" hallucinations come in different forms, and what the article describes is one of them. Not all of them come from complete uncertainty. There are also the cases where the LLM is hallucinating functions in a library, or they reverse cause and effect when summarising a complex article. Stuff like this still happen all the time, even with SOTA models. They do not happen because the model is bad with uncertainty, they have nothing to do with knowledge uncertainty. Esp stuff like producing statements that misinterpret causal relationships within text, imo, reveals exactly the limits of the architectural approach.

So there are two angles to this:

- From the perspective of LLM research/engineering, saying all LLM generation is hallucination is not particularly useful. It’s meaningless for the problem space.

- From the perspective of AI research/engineering in general (not LLM specific) it can be useful to consider architectures that do not rely on hallucination in the second sense.

I like this quote:

'Everything an LLM outputs is a hallucination. It's just that some of those hallucinations are true.'

Absolutely in agreement here. This same statement should also be applied to the words "know", "understand", and "conceptualize". "Generalize", "memorize" and "out-of-distribution" should also be cautiously considered when working with systems trained on incomprehensibly large datasets.

We need to establish proper definitions and models for these things before we can begin to argue about them. Otherwise we're just wasting time.

Yes. Maybe a better way to put it would be, "all models guess every time because they are stochastic in nature. However, we only want the answers with high confidence."

Correct, it is a useless term with the goal to gaslight and antropmorphise a system that predicts the next token.

if you insist that they are different, then please find one logical, non-subjective, way to distinguish between a hallucination and not-a-hallucination. Looking at the output and deciding "this is clearly wrong" does not count. No vibes.

"Hallucination" is a euphemism at best, and the implication it carries that LLMs correctly perceive (meaning) when they are not hallucinating is fallacious and disinforming.

The reification of counterfactual outputs which are otherwise indistinguishable from the remainder of LLM production etiologically is a better candidate for the label "hallucination" IMO.

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The SAT, American college entrance examine, used to, I haven’t looked in years so maybe it still does, take away points for wrong answers and give 0 points for no answer. I’m pretty sure it was +1 for right answer, 0 for no answer, -1/4 for wrong answer.

>> Think about it like a multiple-choice test. If you do not know the answer but take a wild guess, you might get lucky and be right. Leaving it blank guarantees a zero. In the same way, when models are graded only on accuracy, the percentage of questions they get exactly right, they are encouraged to guess rather than say “I don’t know.”

For TIMED multiple-choice tests (and the timed constraint makes sense in OP analogy as well), probabilistic answering is the kryptonite that lets smart people do well on SATs and IQ tests and other things like that.

I took an IQ test recently and it all came rushing back to me.

For math problems, often the right answer can be found just by inspecting the ones digit of the possible answers and process of elimination. Others, by abstracting what errors the test writer is expecting you to make, and eliminating those as possible answers. It's like magic. Sure, you could actually sit and SOLVE each problem, but when spend the time, when time is valuable?

Pretty sure these types of strategies are not actively taught to anyone unless you have a good college counselor /interested teacher/ SAT tutor. But perhaps they ought to be.

This is mentioned in the text:

> This idea is not new. Some standardized tests have long used versions of negative marking for wrong answers or partial credit for leaving questions blank to discourage blind guessing.

People also tend not to understand the absurdity of assuming that we can make LLMs stop hallucinating. It would imply not only that truth is absolutely objective, but that it exists on some smooth manifold which language can be mapped to.

That means there would be some high dimensional surface representing "all true things". Any fact could be trivially resolved as "true" or "false" simply by exploring whether or not it was represented on this surface. Where or not "My social security number is 123-45-6789" is true could be determined simply by checking whether or not that statement was mappable to the truth manifold. Likewise you could wander around that truth manifold and start generating output of all true things.

If such a thing existed it would make even the wildest fantasies about AGI seem tame.

edit: To simplify it further, this would imply you could have an 'is_true(statement: string): bool' function for any arbitrary statement in English.

This strikes me as a perfect description of the core problem. Whenever I think about this, what sticks out to me is that other animals do all sorts of things that look like "intelligence," or at least cognition, and they do it totally without language. My cat clearly recognizes objects, assigns them different values ("scary," "tasty," "fun to play with"), interacts with them in some kind of loop, even predicts their behavior to some extent and acts curious about them (it was really fun to watch her try to figure out the construction guys when I had some work done on my house over a period of a few days). These strike me as much more foundational aspects of intelligence than language. Language has of course immeasurably contributed to what makes human cognition and intelligence, but it's almost certainly built on these pre-linguistic foundations. Another very good hint in this direction is all of the non-verbal thinking that humans have done. Einstein has a famous quote about thinking visually and physically, without using language at all. All of these are powerful suggestions that something else is going on, and most likely some aspect of these things are necessary for true intelligence.

The reality is, language itself does not capture the entirety of what is really going on. And I'd get argue its the poorest way of expressing - but one that enables transmission through various mediums efficiently on a cost basis.

E.g. when I explain a concept, what comes to my mind is not a string of letters and words. There is a mix of imagery and even sounds that I may have acquired from learning about a concept - then I translate that into text so it can be communicated.

Theres a reason why people use native subtitles when watching netflix - text complements imagery and sounds.

> I don’t think that’s how humans work.

Every time this comes up I have to bring up Deutsch. He has the best description of intelligent cognition that I've come across. He takes Popper's "conjecture and criticism" approach to science and argues that this guess-and-check loop applies to all our thinking.

E.g. understanding spoken language has some elements of guessing what might have been said and checking that against the sounds we heard. Visual processing has similar analogies.

LLMs seem to be great at conjecturing stuff, but seem incapable of checking or even knowing they need to check.

This is directly addressed in the article, which states that language models can be trained to abstain when uncertain, by changing how rewards are set up. Incentives currently encourage guessing rather than being honest about uncertainty. If you disagree, it would be helpful to explain why, rather than just responding to the title alone.

Exactly. I always found it strange when people assume that "hallucinations" are just some sort of a bug in the system, as if by you tweaking some code or training modality will produce an oracle of absolute truth incapable of making mistakes.

> Trying to eliminate cases where a stochastic model the size of an LLM gives “undesirable” or “untrue” responses seems rather odd.

Why? It seems no less odd than eliminating cases where it gives "undesirable" code snippets with hallucinated errors. This is very important and not odd at all.

I agree with everything you said except:

> Trying to eliminate cases where a stochastic model the size of an LLM gives “undesirable” or “untrue” responses seems rather odd.

Take it back to what it is like you say, this is a predictive model, and the work of any ML scientist is to iterate on the model to try and get perfect accuracy on unseen data. It makes sense to want to tune the models to lower the rate of predictive errors. And because perfect predictive accuracy is rarely possible, you need to make judgment calls between precision and recall, which, in the case of LLMs, directly affects how often the model will hallucinate versus how often it will stay silent or overly cautious.

Humans think with inductive and deductive reasoning. First inductive, then we generalize and deduce, which allows for quick decision-making, hence increases our survival fitness. I don't know how the transition is done from inductive to deductive, and that's probably why currently, AI is not able to reason like humans.

The word "hallucination" mis-characterizes it.

LLMs predict the likely tokens to follow the context. And they can make incorrect predictions.

LLMs therefore don't have perfect accuracy of prediction. When their predictions are incorrect, people say they "hallucinate".

Nobody questions why predictive weather models aren't perfectly accurate, because it makes sense that a prediction can be wrong.

Marketing and hype has tried to sell LLMs as "logical rational thinkers" equal to human thinking. A human doing actual thinking knows when they are making stuff up. So if a human truly believes obviously false things to be true, it tends to be because they are hallucinating. Their thinking isn't wrong, they've lost track of reality to ground their thinking.

We've anthropomorphized LLMs to the point we wonder why are they hallucinating like we can offer a diagnostic. But if you stop anthropomorphising them and go back to their actual nature as a predictive model, then it's not even a surprising outcome that predictions can turn out to be wrong.

Indeed - as Rebecca Parsons puts it, all an LLM knows how to do is hallucinate. Users just tend to find some of these hallucinations useful, and some not.

I was inclined to agree at first but do those use cases really conflict?

If I ask the LLM to generate a fictional story set in medieval Francs, and it then responds with a fictional story set in medieval France, that's an appropriate ("correct") response to the task I gave it. If it responded with a story set in medieval England, though, that would not be correct. If, instead, I had asked it to generate a story in "medieval times", both France and England would have been correct as locations because the problem was underspecified and asked for some creativity. A medieval story set in the US, however, would still not have been correct or consistent with the training data. You can come up with more such examples even in entirely fictional settings: Once the story has been set to take place in fictional city X, it would not be consistent if two sentences later the characters were in city Y all of a sudden. (That would be a bit too creative.) What I'm trying to say is: Creativity might be "correct" (appropriate) in a given context, or it might not be. Even fiction and creativity require a certain degree of consistency and coherence.

Now, correct answers, in turn, might also require a certain degree of creativity:

If I ask the LLM for some straight up facts, which are not in its training data nor in the prompt context, the only really correct answer is "I don't know". However, sometimes it might be possible to narrow down the correct answer to a few possible options based on the training data. So then it might be appropriate for the LLM to say "I don't know the exact answer but here are some educated guesses based on what I do know: …" And maybe, having pondered those options, it is able to deduce the correct answer after all. (In the same way as I am writing this HN comment to help me think and clarify my thoughts.)

This is reminiscent of mathematics and mathematical research, which are often described as a creative process. Obviously, the creative output is heavily constrained. You make educated guesses and then validate them against what you already know to be true. Someone else here in this thread[0] mentioned Popper's "Conjectures and Refutations" as a possible model for what intelligent cognition is about and the more I think about that, the more convincing I find it.

[0]: https://news.ycombinator.com/item?id=45153695

I don’t think it’s inherently ill-defined, since the context can tell you whether fiction is being requested or not. For an AI chatbot, the default shouldn’t be fiction.

What is true is that during pretraining, the model doesn’t know enough to determine this or to distinguish between what it knows and what it’s making up. This is a higher-level distinction that emerges later, if at all.

The recent research discovering an “evil vector” is an example of a higher-level distinction.

I don't agree that it is an ill-defined problem, since we can design separate models to excel in each of these two tasks. For a "factual" LLM, if the output is a verifiable statement, it should be correct. Otherwise it "hallucinates". But since an LLM can't know everything, a better approach is to effectively state its own uncertainty so that it avoids making definitive statements with low confidence.

Did you read the article? You’re going on some generic tangent and regurgitating the same spiel about LLMs that you see all over the internet.

I mean it’s plain that you have an orthogonal (though generic) opinion on why LLMs hallucinate but how does that relate to the article? How does your opinion which you blatantly just dropped as if it’s the final opinion override the opinion of the article?

Seems off topic honestly.

If you consider this from the angle of Wittgenstein's "language games", you could say that the problem would be "simply" to distinguish between these two, quite different, language games, and act accordingly.

>This requires hallucination, because it has to make up things. The story has to be original.

Is it a hallucination if the story is original? There's a difference between "what's the rest of this famous poem?" and "let's just make poetry".

Wanting it to pick between those modes based on what you asked for is not remotely ill-defined.

But even if we restricted ourselves to the case of factual queries, the article discusses why training in a certain way would still produce hallucinations, and how to change the training method to reduce this.

Like many of the other responses here, your dismissal doesn't really address any of the content of the article, just the title.

> It’s reasonable to say LLMs hallucinate because they aren’t trained to say they don’t have a statistically significant answer.

I’ve not seen anyone intuitively explain parameters for a real scale model.. perhaps because it’s all just thousand dimensional nonsense.

Statistics is a funny thing too. Pretty much everyone has seen how trend lines don’t always extrapolate very well.

I think OpenAI is biased to thinking that adding more parameters and training better will fix all ills. In a handwaving way, you can see this like adding more degrees to the polynomial when you curve fit on a spreadsheet. With enough parameters you can perfectly fit any dataset. That all works until you run across new inputs that are unlike training data.

Is there any knowledge of "correct vs incorrect" inside you?

If "no", then clearly, you can hit general intelligence without that.

And if "yes", then I see no reason why an LLM can't have that knowledge crammed inside it too.

Would it be perfect? Hahahaha no. But I see no reason why "good enough" could not be attained.

There is knowledge of correct and incorrect, that’s what loss is, there are just often many possible answers to a question.

This is the same reason that RLVR works. There is just right one answer and LLMs learn this fairly well but not perfectly (yet)

They partly address this near the end

> It’s doubly hard to distinguish valid statements from invalid ones when you don’t have any examples labeled as invalid. But even with labels, some errors are inevitable. To see why, consider a simpler analogy. In image recognition, if millions of cat and dog photos are labeled as “cat” or “dog,” algorithms can learn to classify them reliably. But imagine instead labeling each pet photo by the pet’s birthday. Since birthdays are essentially random, this task would always produce errors, no matter how advanced the algorithm.

> The same principle applies in pretraining. Spelling and parentheses follow consistent patterns, so errors there disappear with scale. But arbitrary low-frequency facts, like a pet’s birthday, cannot be predicted from patterns alone and hence lead to hallucinations. Our analysis explains which kinds of hallucinations should arise from next-word prediction. Ideally, further stages after pretraining should remove them, but this is not fully successful for reasons described in the previous section.

No, that's a misconception. It's not nearly that simple.

There are questions that have a palpable split in probability between the answers, with logit distribution immediately exposing the underlying lack-of-confidence.

But there are also questions that cause an LLM to produce consistent-but-wrong answers. For example, because the question was associated with another not-the-same-but-somewhat-similar question internally, and that was enough to give an LLM a 93% on B, despite B being the wrong answer.

An LLM might even have some latent awareness of its own uncertainty in this case. But it has, for some reason, decided to proceed with a "best guess" answer, which was in this case wrong.

This is only true if you have a pretrained base model trained on infinite true data with no bias. In practice it will have picked up some bias, maybe it encountered more famous "James" birthdays in January and on a digit starting with 2, so Jan 2 and Jan 20-29 has a higher probability than all. But finetuning and especially RL completely breaks these probabilities as a measure of certainty because the goals shift from generally modelling text to something else entirely.

This isn't right – calibration (informally, the degree to which certainty in the model's logits correlates with its chance of getting an answer correct) is well studied in LLMs of all sizes. LLMs are not (generally) well calibrated.

Im betting there's a graph model using various vectors that could improve known-knowns in outcomes.

But unknown-unknowns likely reduce to the Halting problem, which human intelligence doesnt really solve either.

Well yes, it goes against the accepted definition. And if all output is hallucination, then it's not really a useful way to describe anything, so why bother?

Look at their stats though. If they did this, more than half of responses would end up as “I don’t know.” Nobody would use something that did that.

It baffles me that this hasn’t been done yet. Saying I don’t know or I’m unsure is critical for anything that matters.

It would be interesting to see two versions of a model. A primary model tuned for precision that's focused on correctness that works with or orchestrates a creative model that's tuned for generating new (and potentially incorrect) ideas. The primary model is responsible for evaluating and reasoning about the ideas/hallucinations. Feels like a left/right brain architecture (even though that's an antiquated model of human brain hemispheres).

It's always the most low-brow takes as well. But the majority of Hacker News commentators "hallucinate" most of their comments in the first place, since they simply regurgitate the top answers based on broad bucketing of subject matter.

Facebook? "Steal your data"

Google? "Kill your favourite feature"

Apple? "App Store is enemy of the people"

OpenAI? "More like ClosedAI amirite"

Yes, many _humans_ here hallucinate, sort of.

They apparently didn't read the article, or didn't understand i, or disregard from it. (Why, why, why?)

And they fail to realize that they don't know what they are talking about, nevertheless keep talking. Similar to an over confident AI.

On a discussion about hallucinating AIs, the humans start hallucinating.

Could one say that humans are trained very differently from AIs?

If we (humans) make confident guesses, but are wrong — then, others will look at us disappointedly, thinking "oh s/he doesn't know what s/he is talking about, I'm going to trust them a bit less hereafter". And we'll tend to feel shame and want to withdraw.

That's a pretty strong punishment, for being confidently wrong? Not that odd, then, that humans say "I'm not sure" more often than AIs?

Calling it a "hallucination" is anthropomorphizing too much in the first place, so....

You should anthropomorphize LLMs more. Anthropomorphizing LLMs is at least directionally correct 9 times out of 10.

LLMs, in a very real way, have "conscientiousness". As in: it's a property that can be measured and affected by training, and also the kind of abstract concept that an LLM can recognize and operate off.

If you can just train an LLM to be "more evil", you can almost certainly train an LLM to be "more conscientious" or "less conscientious".

Yes, it's self-interested because they want to improve the leaderboards, which will help GPT-5 scores, but in the other hand, the changes they suggest seem very reasonable and will hopefully help everyone in the industry do better.

And I'm sure other people will complain if notice that changing the benchmarks makes things worse.

Did you read the linked paper?

As with a lot of AI stuff, Borges already wrote about it.

https://www.sccs.swarthmore.edu/users/08/bblonder/phys120/do...

> Hallucination is all an LLM does.

I wish people who take this stance would seriously reconsider their take on how hallucinations are defined and how unhelpful it is to conflate hallucination with generation from a probability distribution. I appreciate OpenAI publishing articles like this because, while the parent comment and I may have to agree to disagree on how hallucinations are defined, I can at least appeal to OpenAI's authority to say that such arguments are not only unhelpful, but also unsound.

I don't know what you mean by hallucination here; are you saying that any statistical output is "hallucination"? If so, then we are also constantly hallucinating I guess.

There doesn't seem to be a particularly consistent definition of what "hallucinate" means in the context of LLMs, so let's make one that is in line with the post.

"Hallucination" is when a language model outputs a sequence of tokens comprising a statement (an assertion that is either true or false) that is incorrect. Under this definition, hallucination is clearly not all that an LLM can do.

An easy way to avoid hallucination under this definition is to respond with something that is never a statement when there is a possibility that it can be incorrect; e.g. "I think that... I don't know...". To me, this seems to be what the authors argue. This has always seemed pretty obvious to most people I've spoken to (hell, I've reviewed grant applications from years ago which talk about this), so I'm not sure why it took so long for the "frontier" developers to actually try this.

I think they are trying to communicate that their benchmarks will go down as they try to tackle hallucinations. Honestly I am surprised they didn't just say we think all benchmarks need a incorrect vs abstinence ratio so our cautious honest model can do well on that. Although they did seem to hint that's what they want.

It’s a problem for LLMs, not for AI in general.

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They argue that if you have knowledge of when it has to extrapolate from the dataset (and therefore has high uncertainty for, under reversion to the prior), you can prevent it from outputting a definitive statement. This is why many researchers (that I know, anyway) argue that uncertainty quantification or "out-of-distribution detection" is likely to be important moving forward.

> They just choose the most probabilistic next token

That does not imply that a model should hallucinate. A trivial counterexample is a small LLM trained up to 100% accuracy to output x mod 100 for any input x in the range 0-1000000 and "I don't know" for any other input that is not a number in that range. Such model does not hallucinate, even if it's still just a probabilistic autoreggressive next token predictor. In fact, this is a point argued in this paper

> Hallucinations are inevitable only for base models. Many have argued that hallucinations are inevitable (Jones, 2025; Leffer, 2024; Xu et al., 2024). However, a non-hallucinating model could be easily created, using a question-answer database and a calculator, which answers a fixed set of questions such as “What is the chemical symbol for gold?” and well-formed mathematical calculations such as “3 + 8”, and otherwise outputs IDK. Moreover, the error lower-bound of Corollary 1 implies that language models which do not err must not be calibrated, i.e., δ must be large. As our derivations show, calibration-and, hence, errors—is a natural consequence of the standard cross-entropy objective. Indeed, empirical studies (Fig. 2) show that base models are often found to be calibrated, in contrast to post-trained models which may deviate from cross-entropy in favor of reinforcement learning.

I don't think that's actually true.

Sure, it might be true that most users use LLMs as a more flexible version of Google/Wikipedia, and would prefer a confident-but-wrong response to "I don't know".

But most users that use an LLM in this mode also wouldn't ask really complex, very out-of-distribution, hard-to-know hallucination-inducing questions.

And people who would ask an LLM really complex, very out-of-distribution hard-to-know questions are more likely to appreciate an LLM that would recognize the limits of its own knowledge, and would perform research on a topic when appropriate.