Isn't that precisely the reason why we introduced the term hallucination? Because llms have historically always made up bullshit of they cannot answer directly... If they now nailed this to maybe the model not respond instead of responding incorrectly, then a lot of previously unusable usecases would become feasible.

So I feel like that's exactly the right metric and the way to track it wrt hallucinations.

I had a buddy in high school that was notorious for doing the same thing. (He's now a senior director at a Big 4 consultancy. :) )

Do you mind expanding a little more?

They had a buddy who used to lie a lot when they were younger… now they get paid for it

The point is that it's not a useful metric on its own. For example, redirecting from /dev/null also achieves a zero hallucination rate.

We want the hallucination rate to decrease while the overall answer rate of queries remains sufficiently high. For more specifics, look into ROC and AUC.

Yes. A model that can answer "I don't know" would be much more trustable than the current used car salesman we have now.

Its very annoying this has been in the capability of models since the very beginning. It could check how probable its token values are and if those fall below a certain threshold either say "I don't know", or output the most probable (well, more like least improbable) tokens but give a very clear, very strong warning that it is a shot in the dark and likely to contain hallucinations.

But no, Google and OpenAI would rather always have an answer ready and tell you to mix glue into your pizza toppings :)

Yeah, I never understood why the top n statistics weren't included in the chat interfaces, to color the text!

I don't have much to add other than this observation that we seem to have moved away from eating one small rock per day for nutritional value, and adding gasoline in spaghetti.

The glue on pizza reference brought back memories :)

It can't, because top n isn't always reliable.

Hallucination detection is an open problem. If it were that simple, people would indeed "just" do it.

Basically the problem is that LLMs aren't trained on things they don't know; an alternative way of saying this is that they're not trained on things they're not trained on, which is obviously true.

When you RL a model and it answers incorrectly, you don't teach it to answer "I don't know", you teach it to answer correctly instead. This makes it very hard for it to realize when it doesn't know things.

Models tend to default to their training data even when they lack sufficient context, they've never been trained to recognize their own uncertainty, so they hallucinate confidently instead.

The probability of tokens is unfortunately a poor proxy for confidence because it is entirely possible for "mixing glue" to appear in a sentence about making pizza depending on context. It might even be likely if the user has asked the model to lie.

Models can answer "I don't know". Hallucination benchmarks, including this, give the models the option to "not attempt". It's just that the metric linked doesn't take into account the rate of correct answers at all. It has its uses in analyzing incorrect vs not attempted answers, but gives a very partial picture.

I think that's what the Omniscience Index is for:

https://artificialanalysis.ai/evaluations/omniscience#aa-omn...

It rewards correct answers and penalizes hallucinations, and finally no reward for refusing to answer.

It's interesting just how poorly some popular Chinese models fare in this regard, like GLM 5.1 or DeepSeek 4 Pro.

Gemini 3.x has truly remarkable knowledge given how it leads in this benchmark despite being (quite a bit) more prone to hallucinate than Claude Opus.

> by refusing to answer all questions.

Cool, precisely the thing other AI is too stupid to do when they don't have the necessary knowledge.

Yes, that's in fact precisely the desired behavior when a model doesn't know the answer.