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GPT-5.2

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Re: GPT-5.2

#931

Post by tsunamifury »

ssl-3 wrote: post #827
rimeice wrote: post #793

Earlier quoted context omitted.

I still don’t really get this argument/excuse for why it’s acceptable that LLMs hallucinate. These tools are meant to support us, but we end up with two parties who are, as you say, prone to “hallucination” and it becomes a situation of the blind leading the blind. Ideally in these scenarios there’s at least one party with a definitive or deterministic view so the other party (i.e. us) at least has some trust in the…

Have you ever employed anyone? People, when tasked with a job, often get it right. I've been blessed by working with many great people who really do an amazing job of generally succeeding to get things right -- or at least, right-enough. But in any line of work: Sometimes people fuck it up. Sometimes, they forget important steps. Sometimes, they're sure they did it one way when instead they did it some other way and…

As far as I can tell (as someone who worked on the early foundation of this tech at Google for 10 years) making up “shit” then using your force of will to make it true is a huge part of the construction of reality with intelligence.

Will to reality through forecasting possible worlds is one of our two primary functions.

Re: GPT-5.2

#932

Post by [deleted] »

delifue wrote: post #588
nbardy wrote: post #398

Those arc agi 2 improvements are insane. Thats especially encouraging to me because those are all about generalization. 5 and 5.1 both felt overfit and would break down and be stubborn when you got them outside their lane. As opposed to Opus 4.5 which is lovely at self correcting. It’s one of those things you really feel in the model rather than whether it can tackle a harder problem or not, but rather can I go back…

It's also possible that OpenAI use many human-generated similar-to-ARC data to train (semi-cheating). OpenAI has enough incentive to fake high score. Without fully disclosing training data you will never be sure whether good performance comes from memorization or "semi-memorization".

[deleted]

Re: GPT-5.2

#934

Post by jakewins »

tsunamifury wrote: post #927
EastLondonCoder wrote: post #856

Earlier quoted context omitted.

I’m with the people pushing back on the “confidence scores” framing, but I think the deeper issue is that we’re still stuck in the wrong mental model. It’s tempting to think of a language model as a shallow search engine that happens to output text, but that metaphor doesn’t actually match what’s happening under the hood. A model doesn’t “know” facts or measure uncertainty in a Bayesian sense. All it really does is t…

Hallucinations are a feature of reality that LLMs have inherited. It’s amazing that experts like yourself who have a good grasp of the manifold MoE configuration don’t get that. LLMs much like humans weight high dimensionality across the entire model then manifold then string together an attentive answer best weighted. Just like your doctor occasionally giving you wrong advice too quickly so does this sometimes eithe…

I asked Gemini the other day to research and summarise the pinout configuration for CANbus outputs on a list of hardware products, and to provide references for each. It came back with a table summarising pin outs for each of the eight products, and a URL reference for each.

Of the 8, 3 were wrong, and the references contained no information about pin outs whatsoever.

That kind of hallucination is, to me, entirely different than what a human researcher would ever do. They would say “for these three I couldn’t find pinouts” or perhaps misread a document and mix up pinouts from one model for another.. they wouldn’t make up pinouts and reference a document that had no such information in it.

Of course humans also imagine things, misremember etc, but what the LLMs are doing is something entirely different, is it not?

Re: GPT-5.2

#935

Post by SubiculumCode »

nrds wrote: post #681
SubiculumCode wrote: post #586

Earlier quoted context omitted.

I constantly switch out, even when it's on the same topic. It starts forming its own 'beliefs and assumptions', gets myopic. I also make use of the big three services in turn to attack ideas from multiple directions

> beliefs and assumptions Unfortunately during coding I have found many LLMs like to encode their beliefs and assumptions into comments; and even when they don't, they're unavoidably feeding them into the code. Then future sessions pick up on these.

YES! I've tried to provide instructions asking it to not leave comments at all.

Re: GPT-5.2

#936

Post by jknightco »

balder1991 wrote: post #888
conception wrote: post #877

Earlier quoted context omitted.

You need to change the temperature to 0 and tune your prompts for automated workflows.

It doesn’t really solve it as a slight shift in the prompt can have totally unpredictable results anyway. And if your prompt is always exactly the same, you’d just cache it and bypass the LLM anyway. What would really be useful is a very similar prompt should always give a very very similar result.

This doesn't work with the current architecture, because we have to introduce some element of stochastic noise into the generation or else they're not "creatively" generative.

Your brain doesn't have this problem because the noise is already present. You, as an actual thinking being, are able to override the noise and say "no, this is false." An LLM doesn't have that capability.

Re: GPT-5.2

#937

Post by adventured »

xwolfi wrote: post #806
khafra wrote: post #715

Earlier quoted context omitted.

LLMs are an especially tough case, because the field of AI had to spend sixty years telling people that real AI was nothing like what you saw in the comics and movies; and now we have real AI that presents pretty much exactly like what you used to see in the comics and movies.

But it cannot think or mean anything, it's just a clever parrot so it's a bit weird. I guess uncanny is the word. I use it as google now, like just to search stuff that are hard to express with keywords.

99% of humans are mimics, they contribute essentially zero original thought across 75 years. Mimicry is more often an ideal optimization of nature (of which an LLM is part) rather than a flaw. Most of what you'll ever want an LLM to do is to be a highly effective parrot, not an original thinker. Origination as a process is extraordinarily expensive and wasteful (see: entrepreneurial failure rates).

How often do you need original thought from an LLM versus parrot thought? The extreme majority of all use cases globally will only ever need a parrot.

Re: GPT-5.2

#938

Post by johnisgood »

Libidinalecon wrote: post #847
rimeice wrote: post #793

Earlier quoted context omitted.

I still don’t really get this argument/excuse for why it’s acceptable that LLMs hallucinate. These tools are meant to support us, but we end up with two parties who are, as you say, prone to “hallucination” and it becomes a situation of the blind leading the blind. Ideally in these scenarios there’s at least one party with a definitive or deterministic view so the other party (i.e. us) at least has some trust in the…

"The airplane wing broke and fell off during flight" "Well humans break their leg too!" It is just a mindlessly stupid response and a giant category error. The way an airplane wing and a human limb is not at all the same category. There is even another layer to this that comparing LLMs to the brain might be wrong because the mereological fallacy is attributing the brain "thinks" vs the person/system as a whole thinks…

You are right that the wing/leg comparison is often lazy rhetoric: we hold engineered systems to different failure standards for good reason.

But you are misusing the mereological fallacy. It does not dismiss LLM/brain comparisons: it actually strengthens them. If the brain does not "think" (the person does), then LLMs do not "think" either. Both are subsystems in larger systems. That is not a category error; it is a structural similarity.

This does not excuse LLM limitations - rimeice's concern about two unreliable parties is valid. But dismissing comparisons as "category errors" without examining which properties are being compared is just as lazy as the wing/leg response.

Re: GPT-5.2

#939

Post by sheeshe »

jknightco wrote: post #936
balder1991 wrote: post #888

Earlier quoted context omitted.

It doesn’t really solve it as a slight shift in the prompt can have totally unpredictable results anyway. And if your prompt is always exactly the same, you’d just cache it and bypass the LLM anyway. What would really be useful is a very similar prompt should always give a very very similar result.

This doesn't work with the current architecture, because we have to introduce some element of stochastic noise into the generation or else they're not "creatively" generative. Your brain doesn't have this problem because the noise is already present. You, as an actual thinking being, are able to override the noise and say "no, this is false." An LLM doesn't have that capability.

Well that’s because if you look at the structure of the brain there’s a lot more going on than what goes on within an LLM.

It’s the same reason why great ideas almost appear to come randomly - something is happening in the background. Underneath the skin.

Re: GPT-5.2

#940

Post by dontlikeyoueith »

goobatrooba wrote: post #359

I feel there is a point when all these benchmarks are meaningless. What I care about beyond decent performance is the user experience. There I have grudges with every single platform and the one thing keeping me as a paid ChatGPT subscriber is the ability to sort chats in "projects" with associated files (hello Google, please wake up to basic user-friendly organisation!) But all of them * Lie far too often with confi…

> Refuse to express uncertainty or nuance (i asked ChatGPT to give me certainty %s which it did for a while but then just forgot...?)

They're literally incapable of this. Any number they give you is bullshit.

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