I like that OpenAI is drawing a clear line on what “hallucination” means, giving examples, and showing practical steps for addressing them. The post isn’t groundbreaking, but it helps set the tone for how we talk about hallucinations.

What bothers me about the hot takes is the claim that “all models do is hallucinate.” That collapses the distinction entirely. Yes, models are just predicting the next token—but that doesn’t mean all outputs are hallucinations. If that were true, it’d be pointless to even have the term, and it would ignore the fact that some models hallucinate much less than others because of scale, training, and fine-tuning.

That’s why a careful definition matters: not every generation is a hallucination, and having good definitions let us talk about the real differences.

> 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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