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GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2
201–210 of 318 posts
Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2
#202Earlier quoted context omitted.
Let's pretend I am someone who has heard people talk about ChatGPT, but have on idea what it actually is. I go to the website and am not presented with any information, just a prompt. So I ask it what it is and what it can do for me. My ask: > In a couple sentences, explain to me the product I'm being sold with ChatGPT. What does it do for me? The Reply from ChatGPT: > ChatGPT is a conversational AI that helps you th…
In this hypothetical case of a us being new users, you now know it's a conversational AI, so you continue asking: > Can I trust the output you give me? And I assume it explains what to trust VS not. I think in the bottom you should also see something like "Any text can contain mistakes" or similar too, which I know is a far cry from what some people push in the press in regards to capabilities, but I still don't see…
I don't think coming at it from the perspective of a new user is that hypothetical. All current users were new users in just the last 3 years. There are still a significant number of people who have heard of it, but haven't used it, or are still very new to it.
I'm not sure why "can I trust the output you give me?" would be a logical followup to the first response it gave me, seeing as it's response didn't say anything about hallucinations or mistakes. It said it could do "useful work" with all kinds of examples, including "specialized knowledge work".
The note under the text field, in gray as to not draw the user's attention, feels more like a CYA line from the lawyers, rather than an instruction they really want users to take to heart. That line also doesn't appear on the main home page. I only shows up after the first prompt is submitted and focus shifts to the conversation. I don't think a CYA line in gray fine print is enough to make users understand it's a plausible-sounding text generation machine instead of an answer machine. Even if I ask that point blank it gives a wordy... yes, but not really, it's being debated by philosophers... response.
Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2
#203Hallucination 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 are…
I often wonder how humans "know" things. I suspect (ignorant armchair) we have some ability to signal strength of those facts, via repetition. Without this layer of introspection i imagine LLMs can never avoid hallucination.
It obviously breaks down with humans too, given we so easily hallucinate and confuse things we "know". However i still suspect we're more reliable at probing information we've experienced vs not. Even if the case of poisoned knowledge, eg a crime scene accidentally implying information to a witness that the witness doesn't actually know, we still "know" that poisoned information via incorrect inference. Ie we "experienced" it.
Wonder what architecture would allow for this style of information/weight probing for an LLM.
Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2
#204> A shift is happening among major AI labs, who are becoming increasingly skeptical of endless parameter count and training data scaling. The limits of this paradigm were put on the world’s stage when Claude Fable 5 was restricted by the US government just three days after its release, marking the first US AI ban stemming from national security. One of the biggest models in the world was banned because a single jailbreak was too much of a risk.
Such a weird thing to start with. The legal status of Fable does not mean that it's not intelligent. If anything, the problem is the opposite, someone thinks it's too intelligent (and/or that Anthropic wouldn't share its last gen intelligent models on the terms the government demanded).
Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2
#205> 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…
>> it is clear that actual intelligence has plateaued significantly.
> These are wild claims -
Indeed, it is not clear there was any actual intelligence at any point.
A lot of generated content sure, sometimes even useful, but not necessarily anything more.
Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2
#206Earlier quoted context omitted.
I'd have to imagine there are wildly diminishing marginal returns to additional SFT/post-training passes. There are a bounded number of (useful) derivations/combinations of Duff's device. If Frontier Labs wish to reduce hallucinations on factual things, they will have to hire people (or the data providers will need to) to do fundamental research above and beyond what is available in extant literature and the web. IE…
As a side gig, I write novel software that solves problems no existing software does, that existing LLMs have difficulty reproducing, purely for the purpose of existing as LLM training data. There are journalists being hired to write Atlantic-worthy articles that exist only as LLM training data, because they're getting paid more than the Atlantic would pay them for it. It's insane. Yes, they are hiring the experts th…
I'm not saying they are not trying - I'm saying we're inventing new problems faster than any Lab can:
1) Identify the gaps
2) Determine how to fix them
3) Implement a fix (especially if that fix is: identify and find experts)
4) And judge the result
How do they know [person] is an expert in [some field]? How do they find that person? How many experts are necessary to give the right information? How do we evaluate the results, especially if it's novel?
You can find a lot of people who disagree on many topics, and those turtles go all the way down.
I'm not in disagreement that your work will help reduce hallucinations and improve model performance! It is.
I predict (I hope I'm wrong!) that we're going to hit some asymptote that is not at 0% hallucinations (and I would even put a substantial nonzero probability that "overall" hallucination rate bottoms out at some minimum and then slowly grows because we just can't keep up with the new garbage we throw at it).
Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2
#207Earlier quoted context omitted.
But if an LLM says "I don't know" should you pay for the tokens?
Depends on what your understanding of the product is. If someone sold you a "Solved all your problems" machine, and it suddenly doesn't solve all your problems, then probably no, you shouldn't pay. But the way I'm being sold LLMs, is basically "A text generator that gives your plausible-sounding human text that sometimes hallucinates and gets things wrong, based on your input", then regardless of what the outcome is,…
They are selling the former to investors, while selling the latter to us.
Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2
#208Earlier quoted context omitted.
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"
What were the alternatives?
Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2
#209Earlier 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…
That doesn’t help the developers who have high standards.
Re: GPT-5.5 hallucinates 3x more than MIT-licensed GLM-5.2
#210Earlier quoted context omitted.
As a side gig, I write novel software that solves problems no existing software does, that existing LLMs have difficulty reproducing, purely for the purpose of existing as LLM training data. There are journalists being hired to write Atlantic-worthy articles that exist only as LLM training data, because they're getting paid more than the Atlantic would pay them for it. It's insane. Yes, they are hiring the experts th…
I'm not saying they are not trying - I'm saying we're inventing new problems faster than any Lab can: 1) Identify the gaps 2) Determine how to fix them 3) Implement a fix (especially if that fix is: identify and find experts) 4) And judge the result How do they know [person] is an expert in [some field]? How do they find that person? How many experts are necessary to give the right information? How do we evaluate the…
> How do they know [person] is an expert in [some field]? How do they find that person?
They have a PhD from a top school, they are a licensed attorney, they are a licensed physician, a board certified cardiologist, etc.
They are constantly recruiting from these populations with well-paying side gigs.
> 4) And judge the result
That's what they pay the experts for. And to have experts review the other experts with peer review.
> You can find a lot of people who disagree on many topics, and those turtles go all the way down.
Which is why everything has to be well-calibrated and not just a hot take - a well reasoned opinion any expert would find fair.
Noone is really caring about hallucinations on point facts these days though, it is much more about complex reasoning tasks. Can they move the bar on the complexity of software LLMs do on their own? Can they get to a point where LLMs can begin to replace physicians? Financial advisors? Actuaries? etc.