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GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2

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Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2

#151

Post by Phelinofist »

blurbleblurble wrote: post #149
wolttam wrote: post #113

> it is clear that actual intelligence has plateaued significantly. > Moving forward, the industry cannot continue to train bigger and bigger models since their intelligence not only plateaus but often will get worse These are wild claims - why are we concluding that bigger models and more data = more hallucination? That’s actually the opposite of what’s been happening over the last couple years. Some models may stil…

How do we know gpt 5.5 is a bigger model

Since it was created by _Open_AI surely it's really open and we can check, right? SCNR

Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2

#152

Post by verdverm »

flexagoon wrote: post #143
verdverm wrote: post #141

Earlier quoted context omitted.

We don't have to listen to these people and can form our own perspectives. Following bad leaders is something to avoid

I agree, but I was responding to the question of why people expect LLMs to be like the star trek computer, and the answer is "because people making and promoting those LLMs claim they are like that"

It is unclear if GP is referring to the global we or the HN we. I leaned towards the latter and injected our knowledge and understandings into the basis for my comment. HN recognizes what's going on

Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2

#154

Post by jonathanhefner »

omneity wrote: post #96
stalfie wrote: post #60

One thing I wonder about hallucinations, is that it seems on the surface that it is an easy problem for RLVR to target. Since you're already generating enormous amounts of reasoning traces which are verified by correct answers, just have "don't know" as an option as a valid answer, and on problems where none of the thousands of reasoning traces led to a correct answer, just promote the traces that led to the "don't k…

It’s not as simple. I trained an LLM before on exactly this, to scratch the itch of this question. The task was simple, using the MS-MARCO[0] dataset which contains queries, search results, answers, I made a training set that has: 1. Questions paired with real results supporting them (mixed with some irrelevant results), and a correct answer 2. Questions paired only with irrelevant results, with the answer “No answer…

Thank you for sharing! Based on your experience, do you think a two-model system might fare better? For example, two models in serial where the second model is trained to "sniff out" potential hallucinations and fact check them (and possibly iterate with the first model)?

Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2

#155

Post by roenxi »

stalfie wrote: post #147
roenxi wrote: post #97

Earlier quoted context omitted.

If we had a theoretical technique to identify the true and objective reality we'd use it in the courts and laboritories. There is no such technique, but what we do have is 2 techniques that seem work: 1) Has a certain standard of evidence been met? 2) Are the related arguments free of logical inconsistencies? We can train the LLMs to do 2, and maybe even 1 to some extent (exactly what quality of evidence a computer c…

Well, I'd argue that this depends on the field you're investigating. Sometimes you have a way to identify objective reality and sometimes you don't. In mathematics the majority of the field is verifiable in this way. Coding a bit less as it's intersubjective, as and the ideal methodology is subject to taste. But even in muddy fields of reality like medicine, there are objective facts to be found. When someone comes i…

> In mathematics the majority of the field is verifiable in this way.

Does mathematics count as not a hallucination though? Particularly in pure mathematics they take a certain pride coming up with wild concepts as unrooted as possible in anything relevant to human existence. The name of the game is purely about maintaining internal logical consistency - which is something an AI can do while hallucinating.

AI hallucinations in maths might be logically consistent or not be. But in that particular case it starts to get a bit iffy what we call it when someone imagines something that doesn't exist. This gets back to the thing where we can train AIs to be logically consistent, but we can't force that consistency to be grounded in any particular universe. Ie, it'll hallucinate but in a very well rationalised way - coincidentally mimicking how a number of mathematicians seem to approach life.

This is the central issue; there is a very real trade-off between facts and verifiablity. Mathematics is perfectly verifiable because it is fact free. We don't have a reliable general system to verify facts. We do have reliable systems for checking arguments (logic).

Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2

#157

Post by flexagoon »

verdverm wrote: post #152
flexagoon wrote: post #143

Earlier quoted context omitted.

I agree, but I was responding to the question of why people expect LLMs to be like the star trek computer, and the answer is "because people making and promoting those LLMs claim they are like that"

It is unclear if GP is referring to the global we or the HN we. I leaned towards the latter and injected our knowledge and understandings into the basis for my comment. HN recognizes what's going on

> HN recognizes what's going on

Maybe the pre-2024 users do, but I've seen plenty of those exact "frontier models never hallucinate" comments on HN as well

Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2

#159

Post by wizzledonker »

andybak wrote: post #55
gymbeaux wrote: post #17

Earlier quoted context omitted.

Those numbers are abysmal. Should we really be using LLMs to write our code? I have a theory- LLMs can spit out code that gets the job done and looks ok, maybe even great, but contains small “anomalies” that compound over time. An enterprise app developed entirely with LLM-happy devs might end up virtually unmaintainable. I’m not sure how to explain it, but the more I see LLM-written code the more I feel it’s bad cod…

I can't help but feel that people continually underestimate how bad human written code becomes over time. The exception is probably single-person passion projects or open source projects that maintain quality governance over time. I strongly suspect most closed source code developed under commercial or internal pressure is pretty awful after a few years of development. All LLM code has to do is suck less than existin…

I think the real issue might be that how “good” the code is matters less than being able to form a mental model for what the human who wrote the code was “thinking”. If written by a machine, this contract is broken and we get more confused, even if our traditional methods of evaluating the code come out equal.

Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2

#160

Post by mcphage »

jmalicki wrote: post #133
an0malous wrote: post #121

Earlier quoted context omitted.

> why are we concluding that bigger models and more data = more hallucination? That’s not what your quotes said. They said bigger models = plateau in intelligence, nothing about more data or increased hallucinations The relevant quote for what you’re talking about would be: > It’s been proven that when a model is trained on large volumes of highly factual and non-theoretical data, it learns to always have an answer.…

I find these internet arguments talking about LLMs as if they are trained by reading the internet to be wild. Yes, pretraining still exists. But for the past few years, pretraining by reading the internet is just the initial bootstrapping of LLM training. The RL training they get from bespoke training data, with very very different characteristics than what these armchair analyses claim, dominates these days.

Where do they get the bespoke training data from? And how much? I don’t really know anything about this.
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