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- AI will never be fully reliable, and people never will be. But AI is wrong in much less predictable and stable ways, and with much less accountability.
- You have to be specific. You only have vague comments about your own thoughts. We had this discussion with Tomahawks in the ninetees, it is not an easy one. This is like answering how long is a string.
IMHO This feels like a troll, OP have several comments about evil Ukrainian AI drones. I feel it is hard to have a naunced discussion if you only diss one part.
Your question is asking for easy technical opinions about a subject where you seem to have no technical knowledge.
by emj - I'd agree with other posters - I'm seeing less hallucination; probably the model-makers are tuning to reduce that, because people make a big deal about it.
I see it as a small deal - it reminds us to check the responses against references. LLMs are a statistical construction, and everyone accepts without complaint that statistical models have a predicted false-positive and false-negative rate. I think of "hallucination" as a false positive; false negative is no answer when the model could have made a useful response. I think there's some relation between our creativity and the LLM capability we dismissively call hallucination.
by iDon - There are two common ways to spot hallucinations. Talk to it about a topic you know well, or implement what it suggests and get an error or failure.
For the first, we're mostly past the point of just "testing". So I don't see that too much anymore. Mostly I still see that though in bug reports generated by AI by someone else. There is usually some underlying bug being reported, but the AI explanation and "helpful suggestion" is typically inaccurate. Generally, suggested fixes are terrible. (They likely work, but fix a symptom not the cause.)
The second still happens, but with much less regularity for me though. It does make mistakes though.
In areas where I'm not as skilled it's very hard to spot errors. When researching general information I'm mostly accepting it on face value.
I find the bug-report thing really interesting. For lots of simple bugs it's great. For more complex things it seems to be very superficial- if a 0 causes an issue here, add a simple guard for 0. There's no depth of understanding why the value is 0 in the first place, when it should be set. If it can (incorrectly) be 0 here, where else might 0 be impacting the code?
This informs my opinion of vibe coded stuff - where there is no skilled human inspection. I expect that code to be of a poor underlying quality. Especially if it's AI changes to an existing human-coded app.
by bruce511 - It hasn't and likely never will because it is a structural part of how AI works.
You can see proof of this if you ask it obscure enough questions. That doesn't mean obscure scientific questions, I asked it questions regarding ship fits and modules in EVE Online, which is an extremely well-documented videogame. There are hundreds of online tools to help you for different things, mining yield calculators and more.
Well, ChatGPT just made up almost everything. Very confidently. It couldn't even get the damage types right, I was quite shocked.
Hallucination has only be solved for extremely narrow sets of problems, and only partially. Coding is one of those problems.
by kevin061 - There has been quite significant progress.
From my experience it has been largely solved for one significant use case which is chatting to frontier models about the reality as described by public knowledge. 2 years ago models would rely on their training data, today they go out of their way trying to look it up on the Internet and verify thoroughly. I have not had a problem for a very long time.
When working off of limited, private/unverifiable context, LLMs still hallucinate, but again, much less than 2 years ago, and more within a "getting confused where a human would easily get confused" range, rather than "outrageously making things up" range.
by bad_username - We're being forced to use some AI features in our IDE at work these days, and we see it make stuff up all the time. Very unpleasant to work with. It will tell us implementing something a certain way is impossible, when I've implemented it myself that way in the past. It will fabricate reasons why builds are failing. It will make assumptions about database schemas from thin air.
It's like a science fiction writer or an improv actor doing technobabble. If by chance it knows the actual answer it might use it, but even if not it still has to say _something_ that sounds plausible to a layperson. It'll never say it doesn't actually know, because their character it's acting as _should_ know.
by aezart - The problem hasn't been solved, and, by the very nature of what LLMs are, can't be. However, it has gotten a lot better, as models have got much larger and pretraining more sophisticated.
Part of the difficulty--not in solving, but in discussing--is in defining what a hallucination is. On the face of it, it seems straightforward: an obviously counterfactual claim or manifest error of reasoning. However, it's not always that simple. A lot of what people consider to be hallucinations are misattributions, specious diagnoses, strangely lopsided preoccupations, eccentric design choices, needlessly verbose or circuitous output or explanations, a kind of metaphysical conflation of the trivial with the significant, etc.
by abalashov