Evaluating 35 open-weight models across three context lengths (32K, 128K, 200K), four temperatures, and three hardware platforms—consuming 172 billion tokens across more than 4,000 runs—we find that the answer is “substantially, and unavoidably.” Even under optimal conditions—best model, best temperature, temperature chosen specifically to minimize fabrication—the floor is non-zero and rises steeply with context length. At 32K, the best model (GLM 4.5) fabricates 1.19% of answers, top-tier models fabricate 5–7%, and the median model fabricates roughly 25%.
Just for context, this is the error rate when the right answer is provided to the LLM in a document. This means that even when the answer is being handed to the LLM they fail at the rates provided in the article/paper.
Most people interacting with LLMs aren’t asking questions against documents, or the answer can not be directly inferred from the documents (asking the LLM to think about the materials in the documents).
That means in most situations the error rate for the average user will be significantly higher.
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I understood a few of those words.
Basically you’ve validated the study that LLMs make shit up, right?
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Are all outputs hallucinations? It’s just some happen to be correct and some aren’t. It doesn’t know and can’t tell unless it’s specifically told (hence the guard rails).
But if I’ve gotta build so many hand rails (instructions) then is it really “AI”?
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I refuse to call it AI
It’s a LM… Pure and simple. Anyway none of the LMs can come up with theory of relatively (if you gave them all of the known physics up to 1915).
Nor can they play paper scissors rock (they don’t realise it’s pointless).
As far as I can tell they’re wrong more times then they’re right and the only use I have for them is as a glorified search engine (and even then they’re still fricking wrong.
They’re only useful if you already know the answer because if you don’t know the answer you don’t know if they’ve given you the wrong answer.
is “potato frontier” an auto-correct fail for Pareto or a real term? Because if it’s not a real term, I’m 100% going to make it one!
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This is pretty bonkers. How TF are they fabricating answers???
Aka being wrong, but with a fancy name!
When Cletus is wrong because he mixed up a dog and a cat when deacribing their behavior do we call it hallucinating? No.
if you have a lobby you get special names, look at the pharma industry who coined the term “discontinuation syndrome” for a simple “withdrawal”
Accepting concepts like “right” and “wrong” gives those tools way too much credit, basically following the AI narrative of the corporations behind them. They can only be used about the output but not the tool itself.
To be precise:
LLMs can’t be right or wrong because the way they work has no link to any reality - it’s stochastics, not evaluation. I also don’t like the term halluzination for the same reason. It’s simply a too high temperature setting jumping into a closeby but unrelated vector set.
Why this is an important distinction: Arguing that an LLM is wrong is arguing on the ground of ChatGPT and the likes: It’s then a “oh but wen make them better!” And their marketing departments overjoy.
To take your calculator analogy: like these tools do have floating point errors which are inherent to those tools wrong outputs are a dore part of LLMs.
We can minimize that but then they automatically use part of their function. This limitation is way stronger on LLMs than limiting a calculator to 16 digits after the comma though…
What word would you propose to use instead?
Fabrication?
That’s my problem: any single word humanizes the tool in my opinion. Iperhaps something like “stochastic debris” comes close but there’s no chance to counter the common force of pop culture, Corp speak a and humanities talent to see humanoid behavior everywhere but each other. :(
At 32K, the best model (GLM 4.5) fabricates 1.19% of answers
Not bad, I don’t know many people who are 98.81% accurate in their statements.
Calculators are correct 100% of the time.
Calculators are not people, Mr. <1.19%.
That’s right! We should be comparing computers to computers. Well, hardware computers, not people computers.
How much do large language models actually hallucinate when answering questions grounded in provided documents?
Okay, this is looking promising, at least in terms of the most important qualifications being plainly stated in the opening line.
Because the amount of hallucinations/inaccuracies “in the wild” - depending on the model being tested - runs about 60-80%. But then again, this would be average use on generalized data sets, not questions focusing on specific documentation. So of course the “in the wild” questions will see a higher rate.
This also helps users, as it shows that hallucinations/inaccuracies can be reduced by as much as ⅔ by simply limiting LLMs to specific documentation that the user is certain contains the desired information, rather than letting them trawl world+dog.
Very interesting!
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We need to stop calling it hallucinations and say what it is, ERRORS
Hallucinations of LLMs are just one class of errors, and the most dangerous one.
Other stuff like garbeled or repeating output are other errors.
