Is there a way to code an LLM to just say "I don't know" when it is uncertain or reaching some sort of edge?
AI hallucinations: Why LLMs make things up (and how to fix it)
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Re: AI hallucinations: Why LLMs make things up (and how to fix it)
#32Earlier quoted context omitted.
Do you have a link to (or can you put here) that "numbers in boxes" problem?
I'm not them, but I think it's a variation of the subset-sum problem If we modify the question to be "sum to 100" (to just seriously reduce the number of example boxes required) then given: | 50 | 20 | 24 | | 7 | 5 | 1 | | 51 | 51 | 51 | the solution would be | [50] | [20] | [24] | | 7 | [ 5] | [ 1] | | 51 | 51 | 51 | | right | down | win | X | right | up | X | X | X
Correct. That but for 1000. You can build your own with any number of online tools.
I don't have a link because it's part of a lesson plan set behind a payment on teachers pay teachers.
Re: AI hallucinations: Why LLMs make things up (and how to fix it)
#33I just recently showed a group of college students how and why using AI in school is a bad idea. Telling them it's plagiarism doesn't have an impact, but showing them how it gets even simple things wrong had a HUGE impact. The first problem was a simple numbers problem. It's 2 digit numbers in a series of boxes. You have to add numbers together to make a trail to get from left to right moving only horizontally or ver…
I refuse to believe that you did any of this with any of the latest models. Gemini and Chat GPT with search are both perfectly capable of producing decent essays with accurate citations. And the 4o model is extremely good at writing python code that can accurately solve math and logic problems. I asked 4o with search to write an essay about the dangers of smoking, along with citations and quotes from the relevant sou…
It was chat gpt, and it was two days ago. Believe it or not, that doesn't really change anything for me. I absolutely understand what AI is capable of, and a lot of it is really quite something.
But you can't trust it to be accurate. You just can't. Every model will absolutely make shit up at some point.
I liken it to working with a very bright 7 year old. It may sound like it knows what it's saying, and it may be able to spit out facts, but it's very ignorant about most of the world.
Re: AI hallucinations: Why LLMs make things up (and how to fix it)
#34> While the hallucination problem in LLMs is inevitable [0], they can be significantly reduced... Every article on hallucinations needs to start with this fact until we've hammered that into every "AI Engineer"'s head. Hallucinations are not a bug—they're not a different mode of operation, they're not a logic error. They're not even really a distinct kind of output. What they are is a value judgement we assign to the…
Calling them hallucinations was a huge mistake.
The term is actually fine. The problem is when it's divorced from the reality of:
> in some sense, hallucination is all LLMs do. They are dream machines.
If you understand that, then the term "hallucination" makes perfect sense.
Note that this in no way invalidates your point, because the term is constantly used and understood without this context. We would have avoided a lot of confusion if we had based it on the phrase "make shit up" and called it "shit" from the start. Marketing trumps accuracy again...
(Also note that I am not using shit in a pejorative sense here. Making shit up is exactly what they're for, and what we want them to do. They come up with a lot of really good shit.)
Re: AI hallucinations: Why LLMs make things up (and how to fix it)
#35Anyone who has raised a child knows they hallucinate constantly when they are young because they are just doing probabilistic output of things they have heard people say in similar situations and saying words they don't actually understand.
LLMs likely have a similar problem.
Re: AI hallucinations: Why LLMs make things up (and how to fix it)
#36Earlier quoted context omitted.
> The trouble is that we software engineers have spent so long working in an artificially deterministic world that we're not used to designing and evaluating probabilistic quality control systems for computer output. I think that's a mischaracterization and not really accurate. As a trade, we're familiar with probabilistic/non-deterministic components and how to approach them. You were closer when you used quotes aro…
You're missing his point. He's saying if you make a program, you expect it to do X reliably. X may include "send an email, or kick off this workflow, or add this to the log, or crash" but you don't expect it to, for example, "delete system32 and shut down the computer". LLMs have essentially unconstrained outputs where the above mentioned program couldn't possibly delete anything or shut down your computer because no…
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Re: AI hallucinations: Why LLMs make things up (and how to fix it)
#37The article referenced the Oxford semantic entropy study but failed to clarify that the issue greatly simplifies LLM hallucination (making most of the article outdated). When we are not sure of an answer we have two choices: say the first thing that comes to mind (like an LLM), or say "I'm not sure". LLMs aren't easily trained to say "I'm not sure" because that requires additional reasoning and introspection (which i…
> proving that hallucination is conceptually a simple problem.
...proving that this one particular piece of the hallucination problem may be conceptually simple.
FTFY
Re: AI hallucinations: Why LLMs make things up (and how to fix it)
#38> While the hallucination problem in LLMs is inevitable [0], they can be significantly reduced... Every article on hallucinations needs to start with this fact until we've hammered that into every "AI Engineer"'s head. Hallucinations are not a bug—they're not a different mode of operation, they're not a logic error. They're not even really a distinct kind of output. What they are is a value judgement we assign to the…
Calling them hallucinations was a huge mistake.
It is a good branding, like neural networks, and even artificial intelligence was. The good point is it makes really easy to detect who is a bullshiter and who understand at least very remotely what a LLM is supposed to produce.
Re: AI hallucinations: Why LLMs make things up (and how to fix it)
#39Earlier quoted context omitted.
You're missing his point. He's saying if you make a program, you expect it to do X reliably. X may include "send an email, or kick off this workflow, or add this to the log, or crash" but you don't expect it to, for example, "delete system32 and shut down the computer". LLMs have essentially unconstrained outputs where the above mentioned program couldn't possibly delete anything or shut down your computer because no…
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Re: AI hallucinations: Why LLMs make things up (and how to fix it)
#40To my understanding, the reason why companies don't mind the hallucinations is the acceptable error rate for a given system. Let's say something hallucinated 25% of the time, but if that's ok, then it's fine for a certain product. If it only hallucinates 5% of the time, it's good enough for even more products and so on. The market will just choose the LLM appropriately depended on the tolerable error rate.