Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

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

arrowtsx.dev

Post reply on HN
Print view View original

11–20 of 318 posts

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

#11

Post by sgc »

grayhatter wrote: post #8
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…

> 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. Do you have a cite…

Since models just output the the most probable tokens and you can never accuse them of doing anything other than making it all up, I would like to see these tests run with a prompt that attempts to mitigate hallucination and finishes with something like: "Telling me that you don't have the relevant information or that the task is impossible is extremely useful to me and a valid answer", and see how much that changes the scoring - as well as the usefulness of the answers. There are so many skills like context7 that can be tweaked to improve these results as well.

In other words, you shouldn't choose the model that hallucinates the least without detailed prompting, since a well-crafted agents.md clause should go a long way to improving output, and almost certainly the top scoring order will be different. To the point that I don't find this type of raw comparison useful beyond maybe 'make sure you test that one with more explicit prompts'.

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

#12

Post by grayhatter »

sgc wrote: post #11
grayhatter wrote: post #8

Earlier quoted context omitted.

> 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. Do you have a cite…

Since models just output the the most probable tokens and you can never accuse them of doing anything other than making it all up, I would like to see these tests run with a prompt that attempts to mitigate hallucination and finishes with something like: "Telling me that you don't have the relevant information or that the task is impossible is extremely useful to me and a valid answer", and see how much that changes…

> In other words, you shouldn't choose the model that hallucinates the least without detailed prompting

You're prompting it wrong is quickly becoming the new, you're holding it wrong.

It's wild how willing software engineers are to blame the user when the actual problem is their own defective design.

Ideally we all, as an industry, will stop accepting this as reasonable excuse for the demonstrated incompetence

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

#13

Post by oshrimpton »

solid_fuel wrote: post #3

> 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. DeepSeek V4 Pro (1.6T params, 49B active, 44 AA Intelligence Index score) has a ludicrous 94% hallucination score on the AA-Omniscience benchmark, meaning on questions that it couldn’t figure out, it only stated that it didn’t know around 6% of the time, and the rest it conf…

Agreed on the title, my bad! But yeah, I've had some truly terrible experiences using these "frontier" models in coding agents especially, where they just fabricate facts about codebases.

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

#14

Post by reinitctxoffset »

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…

Hallucination should be called "failure to ground".

Something about the cost model of US near frontier has the cattle prod out whenever a model is uncertain but thrashes on whether to search. Search flinch is roughly all hallucination.

I don't even wait for the model's turn, if there's a man page or Hoogle hit, stuff the last prefix cache cut point. You come out ahead.

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

#15

Post by aesthesia »

grayhatter wrote: post #8
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…

> 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. Do you have a cite…

This isn't quite the point. When comparing two different models' hallucination rates, the denominator is different. The evaluation works more or less like this: for each question, the model has the option to answer or abstain, so there are three possible outcomes: the model answers and gets it right, the model answers and gets it wrong (hallucination), or the model abstains. The hallucination rate is (model answers wrong) / (model answers wrong or abstains). So if a model A has 50 correct answers, 20 incorrect answers, and 30 abstentions, its hallucination rate is 40%, while a model with 20 correct answers, 20 incorrect answers, and 60 abstentions has a hallucination rate of 25%, even though it hallucinated exactly the same number of times. This is why hallucination rate is incomplete as a metric: it says nothing about the accuracy rate.

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

#16

Post by sudosysgen »

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…

This is missing a common failure mode, which is information past the knowledge cutoff. If you need info past that time they'll fail no matter how big or small the model is, so the hallucination rate can matter independently of the knowledge base. If all use-cases had a uniform risk of falling out of support, this would be a valid argument, but since it's often the case that a datapoint is guaranteed to fall out of support, the absolute ability to recognize that is crucial.

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

#17

Post by gymbeaux »

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…

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 code doing a good job of masquerading as good code. I think this take will become less-hot in the next year or two when we see enterprise greenfield projects that were created entirely with LLM “assistance” go to prod. I think we’ll find that the code is difficult for humans to read, understand, debug, and extend- and I think the larger the codebase the harder it will be for LLMs to maintain. More opportunity for hallucination, larger context windows needed, more tokens bought and spent for smaller and smaller code changes. I think the more code an LLM writes for an app, the worse that codebase becomes.

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

#18

Post by [deleted] »

bastawhiz wrote: post #10
nextaccountic wrote: post #9

>GPT-5.5 and DeepSeek V4 Pro are two of the clearest hallucination leaders, despite being absolutely huge. Because of their immense size they simply did not learn how to say “I don’t know” or recognize intricate logical and technical fallacies. While it is true that a multi-trillion parameter model will always beat a lightweight consumer model on paper (today at least), the commoditization of these huge models is blu…

Now you need a third model to decide if the two other models disagree

[deleted]

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

#19

Post by realusername »

gymbeaux wrote: post #17
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…

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…

> code that gets the job done and looks ok, maybe even great, but contains small “anomalies” that compound over time

They clearly are only assistants for the moment, you can use them to do work ... but only if you could do the said work yourself alone in the first place.

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

#20

Post by Foobar8568 »

gymbeaux wrote: post #17
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…

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…

Have you worked with enterprise apps? The ones I have used for decades are hot garbages.
Post reply on HN