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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

#171

Post by dgellow »

stevemk14ebr wrote: post #169
harrall wrote: post #161

Earlier quoted context omitted.

In cognitive science, it appears your brain has two modes of thinking: - A very parallel type of computation that is fast and generally accurate and integrates hundreds of variables. It’s sometimes labeled as intuition or system 1 thinking. - A much slower, step by step, analytical type, commonly linked with your pre-frontal cortex (one of the newest parts of the brain). Sometimes called system 2 thinking. Maybe the…

An LLM is not thinking, assuming and relating it to thought and universal truths is nonsense.

We inflicted that to ourselves by picking the most confusing terminology ever. "No, reasoning isn't thinking. No when the model says it thinks it's not actually thinking... No an agent isn't actually a creature with agency... No, when we say it hallucinates it doesn't, like, actually hallucinate"

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

#172

Post by aurareturn »

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…

Aren't hallucinations also heavily influenced by compute and memory capacity? IE. Companies can spend more time to verify results in an agentic format, spend more thinking tokens, and less quantization. All of these heavily depend on compute and memory but are proven to decrease hallucinations.

Maybe GPT 5.5 is heavily nerfed due to lack of compute, memory, and energy?

I agree that it's farfetched to conclude that bigger models have pleateued.

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

#173

Post by dgellow »

> One of the biggest models in the world was banned because a single jailbreak was too much of a risk.

We really don't know what the actual reason is given the politics at play. I would bet more on the Trump administration looking for any excuse to punish Anthropic

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

#175

Post by brookst »

stevemk14ebr wrote: post #169
harrall wrote: post #161

Earlier quoted context omitted.

In cognitive science, it appears your brain has two modes of thinking: - A very parallel type of computation that is fast and generally accurate and integrates hundreds of variables. It’s sometimes labeled as intuition or system 1 thinking. - A much slower, step by step, analytical type, commonly linked with your pre-frontal cortex (one of the newest parts of the brain). Sometimes called system 2 thinking. Maybe the…

An LLM is not thinking, assuming and relating it to thought and universal truths is nonsense.

Did you mean sentient?

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

#176

Post by coffeefirst »

an0malous wrote: post #121
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…

> 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.…

Yeah #2 may be incidental. Suppose one lab focused on bigger, and another on reinforcement training geared towards factual accuracy over sycophancy. You could easily wind up with a model from the second lab that is less powerful but more accurate.

I can’t prove it but I suspect there’s a bit of that going on.

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

#177

Post by giancarlostoro »

I wonder if this is what a “Minimally Viable LLM” looks like. I often wonder how much of an LLM do you need before you can just shove a bigger context Window and any dynamic knowledge content to it like a PDF or markdown file to give it knowledge outside of its training data. I feel like LLMs don’t need more data they just need to be refined.

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

#178

Post by themgt »

an0malous wrote: post #121
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…

> 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.…

That’s not what your quotes said. They said bigger models = plateau in intelligence, nothing about more data or increased hallucinations ... I’m pretty sure #1 is well known

Well known in a multiverse branch where Fable was a dud?

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

#179

Post by tasuki »

nutjob2 wrote: post #98
wongarsu wrote: post #95

Earlier quoted context omitted.

Because nearly all benchmarks measure "accuracy" by giving you a point for a correct answer, and 0 points for everything else. If you have 100 questions you are 10% certain on, answering "I don't know" to all of those leads to 0 points, answering all of them as if you are confident leads to an expected value of 10 points. So that's what most AIs are trained to do AA-Omniscience is the only AI benchmark I know of wher…

It should be 1 for correct, 0 for don't know and -1 for wrong. They are much better incentives. In real life a wrong answer is much more damaging than a don't know.

> In real life a wrong answer is much more damaging than a don't know.

I don't know. Is it?

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

#180

Post by ComputerGuru »

aesthesia wrote: post #4

Hallucination rate scores are a little tricky to interpret because they're conditional on the model not knowing the answer. That means they don't measure the probability of your encountering a hallucination in everyday use, since that also depends on the probability of the model not knowing the answer, as well as how well your distribution of tasks aligns with the distribution tested in the eval. I'd also hesitate to…

I’m not disagreeing with you but at the same time, models don’t “know” anything in that binary sense. I’m not trying to get in the woods here, I genuinely mean that what you pass off as a simple explanation is actually incredibly nuanced. A fact appeared once in training data , a fact never appeared in the training data, a fact appeared ten times, a fact appeared a thousand times. Which does the model know? Facts aren’t stored as-is, they’re all broken down into their components and compressed in the weights. “Similar” facts that didn’t appear an overwhelming number of times get bundled together and eventually conflated. But then what is a similar fact? Which facts were entirely ablated vs which were bundled together with others effectively poisoning the pool but also giving it inference strength? The model doesn’t know anything and can never know what it knows or doesn’t know.
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