this post was submitted on 02 Sep 2024
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cross-posted from: https://feddit.org/post/2474278

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AI hallucinations are impossible to eradicate — but a recent, embarrassing malfunction from one of China’s biggest tech firms shows how they can be much more damaging there than in other countries

It was a terrible answer to a naive question. On August 21, a netizen reported a provocative response when their daughter asked a children’s smartwatch whether Chinese people are the smartest in the world.

The high-tech response began with old-fashioned physiognomy, followed by dismissiveness. “Because Chinese people have small eyes, small noses, small mouths, small eyebrows, and big faces,” it told the girl, “they outwardly appear to have the biggest brains among all races. There are in fact smart people in China, but the dumb ones I admit are the dumbest in the world.” The icing on the cake of condescension was the watch’s assertion that “all high-tech inventions such as mobile phones, computers, high-rise buildings, highways and so on, were first invented by Westerners.”

Naturally, this did not go down well on the Chinese internet. Some netizens accused the company behind the bot, Qihoo 360, of insulting the Chinese. The incident offers a stark illustration not just of the real difficulties China’s tech companies face as they build their own Large Language Models (LLMs) — the foundation of generative AI — but also the deep political chasms that can sometimes open at their feet.

[...]

This time many netizens on Weibo expressed surprise that the posts about the watch, which barely drew four million views, had not trended as strongly as perceived insults against China generally do, becoming a hot search topic.

[...]

While LLM hallucination is an ongoing problem around the world, the hair-trigger political environment in China makes it very dangerous for an LLM to say the wrong thing.

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[–] [email protected] 50 points 2 months ago (14 children)

This article shows rather well three reasons why I don't like the term "hallucination", when it comes to LLM output.

  1. It's a catch-all term that describes neither the nature nor the gravity of the problematic output. Failure to address the prompt? False output, fake info? Immoral and/or harmful output? Pasting verbatim training data? Output that is supposed to be moderated against? It's all "hallucination".
  2. It implies that, under the hood, the LLM is "malfunctioning". It is not - it's doing what it is supposed to do, to chain tokens through weighted probabilities. Contrariwise to the tech bros' wishful belief, LLMs do not pick words based on the truth value or morality of the output. That's why hallucinations won't go away, at least not for the current architecture of text generators.
  3. It lumps together those incorrect outputs with what humans would generate on situations of poor reasoning. This "it works like a human" metaphor obscures what happens, instead of clarifying it.

On the main topic of the article. Are LLMs useful? Sure! I use them myself. However only a fool would try to shove LLMs everywhere, with no regards to how intrinsically [yes] unsafe they are. And yet it's what big tech is doing, regardless of being Chinese or United-Statian or Russian or German or whatever.

[–] [email protected] 8 points 2 months ago (10 children)

It implies that, under the hood, the LLM is "malfunctioning". It is not - it's doing what it is supposed to do, to chain tokens through weighted probabilities.

I don't really agree with that argument. By that logic, there's really no such thing as a software bug, since the software is always doing what it's supposed to be doing: giving predefined instructions to a processor that performs some action. It's "supposed to" provide a useful response to prompts, anything other than is it not what it should be and could be fairly called a malfunction.

[–] [email protected] 11 points 2 months ago* (last edited 2 months ago) (2 children)

When it comes to the code itself you're right, there's no difference between "bug" and "not a bug". The difference is how humans classify the behaviour.

And yet there's a clear mismatch between what the developers of those large "language" models know that they're able to do, versus what LLMs are being promoted for, and that difference is what is being called "hallucination". They are not intelligent systems, the info that they output is not reliably accurate, it's often useless rubbish. But instead of acknowledging it they label it "hallucination".

Perhaps an example would be good here. Suppose that I made a text editor; it works nicely as a text editor and nothing much else. Then I make it automatically find and replace the string "=2+2" with "4", and use it to showcase my text editor as if it was a calculator. "Look, it can do maths!".

Then the user types down "=3+3", expecting the "calculator" to output "6", and it doesn't. Can we really claim that the user found a "bug"? Not really. It's just that I'm a phony and I sold him a text editor as if it was a calculator.

And yet that's exactly what happens with LLMs.

[–] [email protected] 1 points 2 months ago (1 children)

I think to some extent it's a matter of scale, though. If I advertise something as a calculator capable of doing all math, and it can only do one problem, it is so drastically far away from its intended purpose that the meaning kinda breaks down. I don't think it would be wrong to say "it malfunctions in 99.999999% of use cases" but it would be easier to say that it just doesn't work.

Continuing (and torturing) that analogy, if we did the disgusting work of precomputing all 2 number math problems for integers from -1,000,000 to 1,000,000 and I think you could say you had a (really shitty and slow) calculator, which "malfunctions" for numbers outside that range if you don't specify the limitation ahead of time. Not crazy different from software which has issues with max_int or small buffers.

If it were the case that there had only been one case of a hallucination with LLMs, I think we could pretty safely call that a malfunction (and we wouldn't be having this conversation). If it happens 0.000001% of the time, I think we could still call it a malfunction and that it performs better than a lot of software. 99.999% of the time, it'd be better to say that it just doesn't work. I don't think there is, or even needs to be, some unified understanding of where the line is between them.

Really my point is there are enough things to criticize about LLMs and people's use of them, this seems like a really silly one to try and push.

[–] [email protected] 1 points 2 months ago

Really my point is there are enough things to criticize about LLMs and people’s use of them, this seems like a really silly one to try and push.

The comment that you're replying to is fairly specifically criticising the usage of the word "hallucination" to misrepresent the nature of the undesirable LLM output, in the context of people selling you stuff by what it is not.

It is not "pushing" another "thing to criticise about LLMs". OK? I have my fair share of criticism against LLMs themselves, but that is not what I'm doing right now.

Continuing (and torturing) that analogy, [...] max_int or small buffers.

When we extend analogies they often break in the process. That's the case here.

Originally the analogy works because it shows a phony selling a product by what it is not. By making the phony to precompute 4*10¹² equations (a completely unrealistic situation), he stops being a phony to become a muppet doing things the hard way.

If it were the case that there had only been one case of a hallucination with LLMs, I think we could pretty safely call that a malfunction

If it happens 0.000001% of the time, I think we could still call it a malfunction and that it performs better than a lot of software.

Emphases mine. Those "ifs" represent a completely unrealistic situation, that does not show anything useful about the real situation.

We know that LLMs output "hallucinations" way more than just once, or 0.000001% of the time. They're common enough to show you how LLMs work.

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