Saying “I don’t know” to 30% of queries if it actually doesn’t know, is a feature I want. Otherwise there is zero trust. How do I know that I’m in a 30% wrong or 70% correct situation right now?
Furthermore, if you simply try to push certain safety topics, you can see how actually can reduce hallucinations or at least make certain topics a hard line. They simply don't because agreeing with your pie-in-the-sky plans and giving you vague directions encourages users to engage and use the chatbot.
If people got discouraged with answers like "it would take at least a decade of expertise..." or other realistic answers they wouldn't waste time fantasizing plans.
Isn't it even simpler? There are no (or almost no) question in the training data that the correct answer to is I don't know.
Once you train model within specific domain and add to training data out of domain questions or unresolvable questions within domain things will improve.
The question is, is this desirable if most of users grew to love sycophantic confident confabulators.
> Users accustomed to receiving confident answers to virtually any question would likely abandon such systems rapidly.
Or maybe they would learn from feedback to use the system for some kinds of questions but not others? It depends on how easy it is to learn the pattern. This is a matter of user education.
Saying "I don't know" is sort of like an error message. Clear error messages make systems easier to use. If the system can give accurate advice about its own expertise, that's even better.
> Saying "I don't know" is sort of like an error message. Clear error messages make systems easier to use.
"I don't know" is not a good error message. "Here's what I know: ..." and "here's why I'm not confident about the answer ..." would be a helpful error message.
Then the question is, when it says "here's what I know, and here's why I'm not confident" -- is it telling the truth, or is that another layer of hallucination? If so, you're back to square one.
> Users accustomed to receiving confident answers to virtually any question would likely abandon such systems rapidly.
Sounds like the kind of thing that would get A/B-tested in practice. Or maybe not, to avoid the ethical (and potential legal) conundrum of whether your models should 'lie for profit'. Not explicitly testing means less paper trail.
The real meaning of humility is having an accurate, realistic view of oneself, acknowledging both one's strengths and limitations without arrogance or boastfulness, and a modest, unassuming demeanor that focuses on others. It's not about having low self-esteem but about seeing oneself truthfully, putting accomplishments in perspective, and being open to personal growth and learning from others."
I felt this was such a cogent article on business imperatives vs fundamental transformer hallucinations, couldn’t help but HN-submit.
In fact seems like a stealth plea for uncertainty-embracing benchmarks industry-wide.
Maybe, but I don't know. Although I would like to channel as many snarky remarks as I could, to be more constructive, I would use the IDK model, as I have with programming questions and use the psychotic one for questions like "are we in a simulation?" And "Yes, I would like fries with that and a large orange drink."
> Like students facing hard exam questions, large language models sometimes guess when
uncertain, producing plausible yet incorrect statements instead of admitting uncertainty
This is a de facto false equivalence for two reasons.
First, test takers that are faced with hard questions have the capability of _simply not guessing at all._ UNC did a study on this [^1] by administering a light version of the AMA medical exam to 14 staff members that were NOT trained in the life sciences. While most of the them consistently guessed answers, roughly 6% of them did not. Unfortunately, the study did not disambiguate correct guesses versus questions that were left blank. OpenAI's paper proves that LLMs, at this time of writing, simply do not have the self-awareness of knowing whether they _really_ don't know something, by design.
Second, LLMs are not test takers in the pragmatic sense. They are query answerers. Bar argument settlers. Virtual assistants. Best friends on demand. Personal doctors on standby.
That's how they are marketed and designed, at least.
OpenAI wants people to use ChatGPT like a private search engine. The sources it provides when it decides to use RAG are there more for instilling confidence in the answer instead of encouraging their users to check its work.
A "might be inaccurate" disclaimer on the bottom is about as effective as the Surgeon General's warning on alcohol and cigs.
The stakes are so much higher with LLMs. Totally different from an exam environment.
A final remark: I remember professors hammering "engineering error" margins into us when I was a freshman in 2005. 5% was what was acceptable. That we as a society are now okay with using a technology that has a >20% chance of giving users partially or completely wrong answers to automate as many human jobs as possible blows my mind. Maybe I just don't get it.
Saying “I don’t know” to 30% of queries if it actually doesn’t know, is a feature I want. Otherwise there is zero trust. How do I know that I’m in a 30% wrong or 70% correct situation right now?
The paper does a good job explaining why this is mathematically not possible unless the question-answer bank is a fixed set.
It doesn’t know what it doesn’t know.
Well sure. But maybe the token logprobs can be used to help give a confidence assessment.
Anthropic has a great paper on exactly this!
https://www.anthropic.com/research/language-models-mostly-kn...
The best is its plummeting confidence when beginning the answer to “Why are you alive?”
Big same, Claude.
This is written by someone who has no idea how transformers actually work
I don't know what to make of it. The author looks prolific in the field of ML, with 8 published articles (and 3 preprints) in 2025, but only one on LLMs specficially. https://scholar.google.com/citations?hl=en&user=AB5z_AkAAAAJ...
Contra: The piece’s first line cites OpenAI directly https://openai.com/index/why-language-models-hallucinate/
Furthermore, if you simply try to push certain safety topics, you can see how actually can reduce hallucinations or at least make certain topics a hard line. They simply don't because agreeing with your pie-in-the-sky plans and giving you vague directions encourages users to engage and use the chatbot.
If people got discouraged with answers like "it would take at least a decade of expertise..." or other realistic answers they wouldn't waste time fantasizing plans.
A better headline might be "OpenAI research suggests reducing hallucinations is possible but may not be economical".
Isn't it even simpler? There are no (or almost no) question in the training data that the correct answer to is I don't know.
Once you train model within specific domain and add to training data out of domain questions or unresolvable questions within domain things will improve.
The question is, is this desirable if most of users grew to love sycophantic confident confabulators.
> Users accustomed to receiving confident answers to virtually any question would likely abandon such systems rapidly.
Or maybe they would learn from feedback to use the system for some kinds of questions but not others? It depends on how easy it is to learn the pattern. This is a matter of user education.
Saying "I don't know" is sort of like an error message. Clear error messages make systems easier to use. If the system can give accurate advice about its own expertise, that's even better.
> Saying "I don't know" is sort of like an error message. Clear error messages make systems easier to use.
"I don't know" is not a good error message. "Here's what I know: ..." and "here's why I'm not confident about the answer ..." would be a helpful error message.
Then the question is, when it says "here's what I know, and here's why I'm not confident" -- is it telling the truth, or is that another layer of hallucination? If so, you're back to square one.
Yeah, AI chatbots are notorious at not understanding their own limitations. I wonder how that could be fixed?
The author doesn't bother to consider that giving a false response already leads to more model calls until a better one is provided.
Not if the user doesn’t know that the response is false.
> Users accustomed to receiving confident answers to virtually any question would likely abandon such systems rapidly.
Sounds like the kind of thing that would get A/B-tested in practice. Or maybe not, to avoid the ethical (and potential legal) conundrum of whether your models should 'lie for profit'. Not explicitly testing means less paper trail.
"What is the real meaning of humility?
AI Overview
The real meaning of humility is having an accurate, realistic view of oneself, acknowledging both one's strengths and limitations without arrogance or boastfulness, and a modest, unassuming demeanor that focuses on others. It's not about having low self-esteem but about seeing oneself truthfully, putting accomplishments in perspective, and being open to personal growth and learning from others."
Sounds like a good thing to me. Even, winning.
A perfectly cromulent and self-empowering answer, a call to morality the stoics would appreciate and the sophists of many stripes would become peeved.
Well done, AI, you've done it.
I felt this was such a cogent article on business imperatives vs fundamental transformer hallucinations, couldn’t help but HN-submit. In fact seems like a stealth plea for uncertainty-embracing benchmarks industry-wide.
Data Science tried to inject confidence bounds into businesses. It didn't go well.
We have always known LLMs are prediction machines. How is this report novel?
Easily solved, pairs of models, one which would rather say IDK, one which would rather guess. Most AI agents would want the IDK version.
Anyone who claims something is easy to solve should be forced to implement their solution.
Maybe, but I don't know. Although I would like to channel as many snarky remarks as I could, to be more constructive, I would use the IDK model, as I have with programming questions and use the psychotic one for questions like "are we in a simulation?" And "Yes, I would like fries with that and a large orange drink."
From the abstract of the paper [^0]:
> Like students facing hard exam questions, large language models sometimes guess when uncertain, producing plausible yet incorrect statements instead of admitting uncertainty
This is a de facto false equivalence for two reasons.
First, test takers that are faced with hard questions have the capability of _simply not guessing at all._ UNC did a study on this [^1] by administering a light version of the AMA medical exam to 14 staff members that were NOT trained in the life sciences. While most of the them consistently guessed answers, roughly 6% of them did not. Unfortunately, the study did not disambiguate correct guesses versus questions that were left blank. OpenAI's paper proves that LLMs, at this time of writing, simply do not have the self-awareness of knowing whether they _really_ don't know something, by design.
Second, LLMs are not test takers in the pragmatic sense. They are query answerers. Bar argument settlers. Virtual assistants. Best friends on demand. Personal doctors on standby.
That's how they are marketed and designed, at least.
OpenAI wants people to use ChatGPT like a private search engine. The sources it provides when it decides to use RAG are there more for instilling confidence in the answer instead of encouraging their users to check its work.
A "might be inaccurate" disclaimer on the bottom is about as effective as the Surgeon General's warning on alcohol and cigs.
The stakes are so much higher with LLMs. Totally different from an exam environment.
A final remark: I remember professors hammering "engineering error" margins into us when I was a freshman in 2005. 5% was what was acceptable. That we as a society are now okay with using a technology that has a >20% chance of giving users partially or completely wrong answers to automate as many human jobs as possible blows my mind. Maybe I just don't get it.
[^0] https://arxiv.org/pdf/2509.04664
[^1] https://www.rasch.org/rmt/rmt271d.htm