[raincole]

I don't know why the narrative became "don't call it hallucination". Grantly English isn't my mother tongue so I might miss some subtlty here. If you know how LLM works, call it "hallucination" doesn't make you know less. If you don't know how LLM works, using "hallucination" doesn't make you know less either. It's just a word meaning AI gives wrong[1] answer.

People say it's "anthropomorphizing" but honestly I can't see it. The I in AI stands for intelligence, is this anthropomorphizing? L in ML? Reading and writing are clearly human activities, so is using read/write instead of input/output anthropomorphizing? How about "computer", a word once meant a human who does computing? Is there a word we can use safely without anthropomorphizing?

[1]: And please don't argue what's "wrong".

[devjab]

I suspect it’s about marketing. I’m not sure it would be so easy to sell these tools to enterprise organisations if you outlined that they are basically just very good at being lucky. With the abstraction of hallucinations you sort of put into language why your tool is sometimes very wrong.

To me the real danger comes from when the models get things wrong but also correct at the same time. Not so much in software engineering, I doubt your average programmer without LLM tools will write “better” code without getting some bad answers. What consents me is more how non-technical departments implement LLMs into their decision making or analysis systems.

Done right, it’ll enhance your capabilities. We had a major AI project in cancer detection, and while it actually works it also doesn’t really work on its own. Obviously it was meant to enhance the regular human detection and anyone involved with the project screamed this loudly at any chance they got. Naturally it was seen as an automation process by the upper management and all the humans parts of the process were basically replaced… until a few years later when we had a huge scandal about how the AI worked as it was meant to do, which wasn’t to be on its own. Today it works along side the human detection systems and their quality is up. It took people literally dying to get that point through.

Maybe it would’ve happened this way anyway if the mistakes weren’t sort of written into this technical issue we call hallucinations. Maybe it wouldn’t. From personal experience with getting projects to be approved, I think abstractions are always a great way to hide the things you don’t want your decision makers to know.

[Affric]

The AI companies don’t want you “anthropomorphising” the models because it would put them at risk of increased liability.

You will be told that linear algebra is just a model and the fact that epistemology has never turned up a decent result for what knowledge is will be ignored.

We are meant to believe that we are somehow special magical creatures and that the behaviour of our minds cannot be modelled by linear algebra.

[ben_w]

I don't see how anthropomorphism reduces liability.

If a company does a thing that's bad, it doesn't matter much if the work itself was performed by a blacksmith or by a robot arm in a lights-off factory.

> We are meant to believe that we are somehow special magical creatures and that the behaviour of our minds cannot be modelled by linear algebra

I only hear this from people who say AI will never reach human level; of AI developers that get press time, only LeCun seems so dismissive (though I've not actually noticed him making this specific statement, I can believe he might have).

[duggan]

No, you’re just meant not to assert that linear algebra is equivalent to any process in the human brain, when the human brain is not understood well enough to draw that conclusion.

[p1esk]

It's just a word meaning AI gives wrong answer.

No, it’s more specific than just wrong.

Hallucination is when a model creates a bit of fictitious knowledge, and uses that knowledge to answer a question.

[olalonde]

Can you give an example of a "wrong" answer vs an "hallucinated" answer?

[omikun]

The issue is there is no difference between a right answer and a hallucinated answer.

[stefanve]

there are many types of a wrong answer, and the difference is based on how the answer came to be. In case of BS/Hallucination there is no reason or logic behind the answer it is basically, in the case of LLM, just random text. There was no reasoning behind the output or it wasn't based on facts.

You can argue if it matters how a wrong answer came about ofc but there is a difference

[influx]

Wrong is code that doesn’t compile. Hallucinated is compilable code using a library that never existed.

[gbnwl]

Can code using a library that doesn't exist compile? I admit ignorance here.

[influx]

No it can't, I should have said code that has valid syntax, but are using APIs or libraries that don't exist.

[ruszki]

It doesn't need to create wrong answers. It's enough to recall people who gave wrong answers.

[ben_w]

I've heard the term originated in image recognition, where models would "see" things that weren't there.

You can still get that with zero bad labels in a supervised training set.

Multiple causes for the same behaviour makes progress easier, but knowing if it's fully solved harder.

[VanillaCafe]

> I don't know why the narrative became "don't call it hallucination".

Context is "don't call it hallicination" picked up meme energy since https://link.springer.com/article/10.1007/s10676-024-09775-5 on the thesis that "Calling their mistakes ‘hallucinations’ isn’t harmless: it lends itself to the confusion that the machines are in some way misperceiving but are nonetheless trying to convey something that they believe or have perceived."

Which is meta-bullshit because it doesn't matter. We want LLMs to behave more factually, whatever the non-factuality is called. And calling that non-factuality something else isn't going to really change how we approach making them behave more factually.

[intended]

How are LLMs not behaving factually? They already predict the next most likely term.

If they could predict facts, then these would be gods, not machines. It would be saying that in all the written content we have, there exists a pattern that allows us to predict all answers to questions we may have.

[ijk]

The problem is that some people are running around and saying they are gods. Which I wouldn't care about, but an alarming number of people do believe that they can predict facts.

[intended]

Our system can effectively predict facts.

It logics its way to it.

By predicting the next word in a sequence of words.

Sure? It kinda sounds plausible? But man, if it’s that straight forward, what have we been doing as a species for so many years ?

[intended]

TLDR TLDR: Assuming we dont argue right/wrong, technically everything an LLM does is a hallucination. This completely dilutes the meaning of the word no?

TLDR: Sure. A rose by any other name would be just as sweet. It’s when I use the name of the rose and imply aspects that are not present, that we create confusion and busy work.

Hey, calling it a narrative is to move it to PR speak. I know people have argued this term was incorrect since the first times it was ever shared on HN.

It was unpopular to say this when ChatGPT launched, because chatGPT was just that. freaking. cool.

It is still cool.

But it is not AGI. It does not “think”.

Hell - I understand that we will be doing multiple columns of turtles all the way down. I have a different name for this approach - statistical committees.

Because we couched its work in terms of “thinking”, “logic”, “creativity”, we have dumped countless man hours and money into avenues which are not fruitful. And this isnt just me saying it - even Ilya commented during some event that many people can create PoCs, but there are very few production grade tools.

Regarding the L in ML, and the I in AI ->

1) ML and AI were never quite as believable as ChatGPT. Calling it learning and intelligence doesnt result in the same level of ambiguity.

2) A little bit of anthropomorphizing was going on.

Terms matter, especially at the start. New things get understood over time, as we progress we do move to better terms. Let’s use hallucinations for when a digital system really starts hallucinating.

[th0ma5]

AI is a nebulous, undefined term, and many people specifically criticize the use of the word intelligent.

[Affric]

Always people who consider themselves intelligent