Hacker News Reader

Comment by qarl | Hacker News Reader

While this is absolutely true - I'd hesitate to discount using similar agents for checking each other. Two agents will almost never hallucinate in the same way, regardless of their weights - and by having a second one (with a different context) check almost entirely eliminates the problem.

View on HN

> and by having a second one (with a different context) check almost entirely eliminates the problem.

You solved one of the largest problems with current LLMs. How is it possible that nobody tried that before?

Because they do. There are already LLMs checking outputs of other LLMs, the bullshit answers that you see are the results of failures on that checks. If you remove all checks LLMs will create hallucinations even more often.

I'm sorry you're so upset, but it's true. Try it for yourself - get your LLM to hallucinate something, and then paste that text into another chat window and ask it to verify the result for you.

It depends what we're judging, doesn't it? If it's "is the formatting in this document compliant with our standards?" I think it's reasonable. If it's like, life-altering if it's wrong I'm less sanguine.

They have already shown algorithmic discrimination in predicting recidivism for brown people, as they are nonsensically overrepresented in the statistical data of US prison populations.

Folks should sue in a class-action lawsuit, any legal firm worth their beautiful walnut desks would seriously be happy take on that constitutionally backed mission. =3

> brown people, as they are nonsensically overrepresented in the statistical data of US prison populations

"Nonsensical?" They commit violent crimes, they get prosecuted for said violent crimes, and are serving prison sentences for those crimes. The algorithm picks up on this trend using the same logic that insurance actuaries use, which has also been largely neutered by critical theory.

What even is the argument here-- they're all innocent? Cops are ignoring piles of dead white people and their white murderers to only go patrol brown neighborhoods? We both know neither claim is true. The usual complaint is that cops avoid their neighborhoods and/or are lazy in investigating the crimes they report. The idea of overpolicing has always been a Marxist double-bind...nonsensical, I daresay.

Oh. Yes. Sure. If there is an error in the training data then they will obviously cite that error.

I'm mostly talking about random coding errors.

No modern LLM can tell how many eyes the magic card Pit Imp has. They all say 2. This is across all reasoning levels and paid Gemini, Claude, and GPT (Sol)

Drawing a line red to split up the image then has them answer correctly.

Their failure modes are highly correlated.

Yes - those failures (like strawberry) are. And those failures are very rare, which is why you had to reach for the Pit Imp MTG card, which I had to Google to understand your point.

But the failures are universal and correlated

Not really.

Cohen, Hamri, Geva & Globerson, "LM vs LM: Detecting Factual Errors via Cross Examination": Cross-examination "detects over 70% of the incorrect claims while maintaining a high precision of >80%".

By design, even the same LLM, when asked the same question multiple times, will almost never hallucinate in the same way. By that (flawed) logic, you could have the same LLM check itself.

You can have the same LLM check itself, especially if you start over with slightly different prompts or context. They sometimes get things right and sometimes don't. Although, if you genuinely have no way to know whether the answer is right or wrong, or the errors are severe, then these checks will not accomplish much. This checking can be better than nothing.

I disagree. When I started having my agents do an independent code review before allowing a commit - the code quality improved dramatically. I don't have numbers... but by and large the errors mostly went away. I went from struggling to get good code to just... getting good code.

Is it perfect? No. But it does what I asked it to do.

I'm confused. How does this disagree with me? You can do the same process with the same model, and that is what I was talking about doing.

Yes - exactly. Because they don't hallucinate in the same way - they are perfect for detecting each other's hallucinations.

But when you ask the SAME LLM (with the same context) they remember the hallucination so it doesn't work. You have to use a fresh one without the same context

> Two agents will almost never hallucinate in the same way, regardless of their weights

Citation needed

Personal experience using agents and seeing this happen frequently.

Try it yourself. Get one to hallucinate, then paste that text into a new window and ask it to verify the facts.

EDIT:

Also - Cohen, Hamri, Geva & Globerson, "LM vs LM: Detecting Factual Errors via Cross Examination": Cross-examination "detects over 70% of the incorrect claims while maintaining a high precision of >80%".

So 70% for ANY error, not just hallucinations.

Reply on news.ycombinator.com