- cross-posted to:
- hackernews@lemmy.smeargle.fans
- cross-posted to:
- hackernews@lemmy.smeargle.fans
Google rolled out AI overviews across the United States this month, exposing its flagship product to the hallucinations of large language models.
Google rolled out AI overviews across the United States this month, exposing its flagship product to the hallucinations of large language models.
Can we swap out the word “hallucinations” for the word “bullshit”?
I think all AI/LLM stuf should be prefaced as “someone down the pub said…”
So, “someone down the pub said you can eat rocks” or, “someone down the pub said you should put glue on your pizza”.
Hallucinations are cool, shit like this is worthless.
No, hallucination is a really good term. It can be super confident and seemingly correct but still completely made up.
That is just being WRONG.
You just described entirety of reddit and last I checked we didn’t call that hallucinating
It’s a really bad term because it’s usually associated with a mind, and LLMs are nothing of the sort.
So is bullshitting. More so, only human minds can bullshit.
We anthropomorphize machines all the time, it’s fine.
I’d prefer we’d start calling all genai output hallucinations again. It used to be like 10 years ago, but somewhere along the line marketing decided hallucinated truths aren’t “hallucinations”.
And a bull’s anus.
It’s fucking not, amd I’m not changing my mind about it.
Anthropomorphication is hard to avoid in AI.
Many worthy things are difficult.
But is anthropomorphism of AI particularly worrying?
It is when the people tends to give more credence to entities that appear sentient and to have agency.
for it to “hallucinate” things, it would have to believe in what it’s saying. ai is unable to think - so it cannot hallucinate
Hallucination is a technical term. Nothing to do with thinking. The scientific community could have chosen another term to describe the issue but hallucination explains really well what’s happening.
huh, i kinda assumed it was a term made up/taken by journalists mostly, are there actual research papers on this using that term?
Yup. Loads of them! https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=hallucinations+llm&btnG=
It used to mean all generated output though. Calling only mistakes hallucinations is new, definitely because of hype.
So how do you prove it can’t think? Or that you actually can?
because it’s a text generation machine…? i mean, i wouldn’t say i can prove it, but i don’t think anyone can prove it’s capable of thinking, much less of reasoning
like, it can string together a coherent sentence thanks to well crafted equations, sure, but i wouldn’t qualify that as “thinking”, though i guess the definition of “thinking” is debatable
It’s an interesting question. I am inclined to believe that the faster it gets at running those equations, over and over and over, reanalysing is data and responses as it goes, that that ultimately leads to some kind of evolution. You know, Vger style.
It can tell you how to stack things on top of each other the best way to get a high tower. Etc.
Those are not random sentences. If you can not define thinking in a way this machine fails at, then stop saying it does not think.
A parrot can be trained to tell you how to stack things on top of each other the best way to get a high tower.
This is just an electronic parrot, millions of times faster to train than the biological parrot, specialized in repetition alone (can’t really do anything else a parrot can) and which has been trained on billions of texts.
You’re confusing one specific form in which humans externally express cogniscence with the actual cogniscence itself: just because intelligence can produce some forms of textual communication doesn’t mean that the relationship holds in the opposite direction and such forms of textual communication require intelligence, or if you will, just because you can photograph a real pizza to get a picture of a pizza doesn’t mean a picture of a pizza is actually of a real pizza and not something with glue to make it look like it has stringy melted cheese.
Again, it is absolutely capable to come up with it’s own logical stuff, hence my example. Stop saying it just copies existing stuff, that is simply wrong.
Sure, whatever.
interesting, in my experience, it’s only been good at repeating things, and failing on unexpected inputs - it’s able to answer pretty accurately if a small number is even or odd, but not if it’s a large number, which indicates it’s not reasoning but parroting answers to me
do you have example prompts where it showed clear logical reasoning?
I think delusion might be a better word. You can hallucinate and know it’s not real
My experience with certain chemicals suggests this is true.
deleted by creator
I want an AI/LLM that has been trained exclusively on the technical documentation and a haynes manual for a make and model of car.
“Hey AI, how do I change the fuel filter and what tools will I need?”
You can sorta get that now if you play with it. I was building a driver a few months back and gave it the PDFs involved.
I don’t even think hallucinations is the right word for this. It’s got a source. It is giving you information from that source. The problem is it’s treating the words at that source as completely factual despite the fact that they are not. Hallucinations from what I’ve read actually is more like when it queries it’s data set, can’t find an answer, and then generates nonsense in order to provide an answer it doesn’t have. Don’t think that’s the same thing.
I don’t even think it’s correct to say it’s querying anything, in the sense of a database. An LLM predicts the next token with no regard for the truth (there’s no sense of factual truth during training to penalize it, since that’s a very hard thing to measure).
Keep in mind that the same characteristic that allows it to learn the language also allows it to sort of come up with facts, it’s just a statistical distribution based on the whole context, which needs a bit randomness so it can be “creative.” So the ability to come up with facts isn’t something LLMs were designed to do, it’s just something we noticed that happens as it learns the language.
So it learned from a specific dataset, but the measure of whether it will learn any information depends on how well represented it is in that dataset. Information that appears repeatedly in the web is quite easy for it to answer as it was reinforced during training. Information that doesn’t show up much is just not gonna be learned consistently.[1]
[1] https://youtu.be/dDUC-LqVrPU
I understand the gist but I don’t mean that it’s actively like looking up facts. I mean that it is using bad information to give a result (as in the information it was trained on says 1+1 =5 and so it is giving that result because that’s what the training data had as a result. The hallucinations as they are called by the people studying them aren’t that. They are when the training data doesn’t have an answer for 1+1 so then the LLM can’t do math to say that the next likely word is 2. So it doesn’t have a result at all but it is programmed to give a result so it gives nonsense.
Yeah, I think the problem is really that language is ambiguous and the LLMs can get confused about certain features of it.
For example, I often ask different models when was the Go programming language created just to compare them. Some say 2007 most of the time and some say 2009 — which isn’t all that wrong, as 2009 is when it was officially announced.
This gives me a hint that LLMs can mix up things that are “close enough” to the concept we’re looking for.