how did LLMs get this far without any concept of understanding? how much further can they go until they become “close enough”?
I think we are way past the point of debate here. LLMs are not stochastic parrots. LLMs do understand an aspect of reality. Even the LLMs that are weaker than sora understand things.
What is debatable is whether LLMs are conscious. But whether it can understand something is a pretty clear yes. But does it understand everything? No.
Both the machine and the human are a black box. The human brain is not completely understood and the LLM is only trivially understood at a high level through the lens of stochastic curve fitting.
When something produces output that imitates the output related to a human that we claim "understands" things that is objectively understanding because we cannot penetrate the black box of human intelligence or machine intelligence to determine further.
In fact in terms of image generation the LLM is superior. It will generate video output superior to what a human can generate.
Now mind you the human brain has a classifier and can identify flaws but try watching a human with Photoshop to try to even draw one frame of those videos.. it will be horrible.
Does this indicate that humans lack understanding? Again, hard to answer because we are dealing with black boxes so it's hard to pinpoint what understanding something even means.
We can however set a bar. A metric. And we can define that bar as humans. all humans understand things. Any machine that approaches human input and output capabilities is approaching human understanding.
There is no such difference, we evaluate that based on their output. We see these massive model make silly errors that nobody who understands it would make, thus we say the model doesn't understand. We do that for humans as well.
For example, for Sora in the video with the dog in the windos, we see the dog walk straight through the window shutters, so Sora doesn't understand physics or depth. We also see it drawing the dogs shadow on the wall very thin, much smaller than the dog itself, it obviously drew that shadow as if it was cast on the ground and not a wall, it would have been very large shadow on that wall. The shadows from the shutters were normal, because Sora are used to those shadows being on a wall.
Hence we can say Sora doesn't understand physics or shadows, but it has very impressive heuristics about those, the dog accurately places its paws on the platforms etc, and the paws shadows were right. But we know those were just basic heuristics since the dog walked through the shutters and its body cast shadow in the wrong way meaning Sora only handles very common cases and fails as soon as things are in an unexpected envionment.
Two things. We also see the model make things that are correct. In fact the mistakes are a minority in comparison to what it got correct. That is in itself an indicator of understanding to a degree.
The other thing is, if a human tried to reproduce that output according to the same prompt, the human would likely not generate something photorealistic and the thing a human comes up with will be flawed, ugly disproportionate wrong and an artistic travesty. Does this mean a human doesn't understand reality? No.
Because the human generates worse output visually than an LLM we cannot say the human doesn't understand reality.
Additionally the majority of the generated media is correct. Therefore it can be said that the LLM understands the majority of the task it was instructed to achieve.
Sora understands the shape of the dog. That is in itself remarkable. I'm sure with enough data sora can understand the world completely and to a far greater degree than us.
I would say it's uncharitable to say sora doesn't understand physics when it gets physics wrong, and that for the things it gets right it's only heuristics.
Sora has zero of this knowledge. This is very much allegory of the cave[1].
If Sora sees a series of images that contain impossible physics, for example MC Escher paintings, what will happen?
A person with no knowledge about mathematical formulas will still be able to recognize impossible MC Escher paintings. With enough data, Sora will be able to generate both impossible and possible physics and know the difference. We can already see in the video that it has a rough understanding of it.
For example, if I asked you whether you “understand” ballistic flight, and you produced a table that you interpolate from instead of a quadratic, then I would not feel that you understand it, even though you can kinda sorta model it.
And even if you do, if you didn’t produce the universal gravitation formula, I would still wonder how “deeply” you understand. So it’s not like “understand” is a binary I suppose.
Thus what output would you expect for either of these boxes to demonstrate true understanding to your question?
Defining "understanding" is difficult (epistemology struggles with the apparently simpler task of defining knowledge), but if I saw a dialogue between two LLMs figuring out something about the external world that they did not initially have much to say about, I would find that pretty convincing.
You are free to disagree with this, but I feel your metric for understanding resembles the Turing test, while the sort of thing I have proposed here, which involves AIs interacting with each other, is a refinement that makes a step away from defining understanding and intelligence as being just whatever human judges recognize as such (it still depends on human judgement, but I think one could analyze the sort of dialogue I am envisioning more objectively than in a Turing test.)
Even if the metric is some side marker where in the future is found to have poor correlation or causation with the the thing being measured the hard metric is still valid.
Take IQ. We assume iq measures intelligence. But in the future we may determine that no it doesn't measure intelligence well. That doesn't change the fact that iq tests still measured something. The score still says something definitive.
My test is similar to the Turing test. But so is yours. In the end there's a human in the loop making a judgment call.
In your final paragraph, you attempt to suggest that my proposed test is no better than the Turing test (and therefore no better than what you are doing), but as you have not addressed the ways in which my proposal differs from the Turing test, I regard this as merely waffling on the issue. In practice, it is not so easy to come up with tests for whether a human understands an issue (as opposed to having merely committed a bunch of related propositions to memory) and I am trying to capture the ways in which we can make that call.
You entered this debate saying "I think we are way past the point of debate here. LLMs are not stochastic parrots. LLMs do understand an aspect of reality", yet your post here ends with "in the end there's a human in the loop making a judgment call", explicitly acknowledging that your strong initial claims are matters of opinion, rather than established facts supported by hard metrics.
No it's not. I based my argument on a concrete metric. Human behavior. Human input and output.
> I regard this as merely waffling on the issue.
No offense intended but I disagree. There is a difference but that difference is trivial to me. To LLMs talking is also unpredictable. LLMs aren't machines directed to specifically generate creative ideas, they only do so when prompted. Left to its own devices to generate random text does not necessarily lead to new ideas. You need to funnel got in the right direction.
>You entered this debate saying "I think we are way past the point of debate here. LLMs are not stochastic parrots. LLMs do understand an aspect of reality", yet your post here ends with "in the end there's a human in the loop making a judgment call", explicitly acknowledging that your strong initial claims are matters of opinion, rather than established facts supported by hard metrics.
There are thousands of quantitative metrics. LLMs perform especially well on these. Do I refer to one specifically? No. I refer to them all collectively.
I also think you misunderstood. Your idea is about judging an whether an idea is creative or not. That's too wishy washy. My idea is to compare the output to human output and see if there is a recognizable difference. The second idea can easily be put into an experimental quantitative metric in the exact same way the Turing test does it. In fact, like you said it's basically just a Turing test.
Overall AI has passed the Turing test but people are unsatisfied. Basically they need to just make a harsher Turing test to be convinced. For example have people directly know the possibility that the thing inside a computer is possibly an LLM and not a person and have the person directly investigate to uncover the true identity. If the LLM can successfully decieve the human consistently then that is literally the final bar for me..
Hey no offense but I don't appreciate this style of commenting where you say it's "odd." I'm not trying to hide evidence from you and I'm not intentionally lying or making things up in order to win an argument here. I thought of this as a amicable debate. Next time if you just ask for the metric rather then say it's "odd" that I don't present it that would be more appreciated.
I didn't present evidence because I thought it was obvious. How are LLMs compared with one another in terms of performance? Usually those are done with quantitative tests. You can feed any number of these tests including stuff like the SAT, BAR, ACT, IQ, SATII etc.
They also have LLM targetted tests as well:
https://assets-global.website-files.com/640f56f76d313bbe3963...
Most of these tests aren't enough though as the LLM is remarkably close to human behavior and can do comparably well and even better than most humans. I mean that last statement I made would usually make you think that those tests are enough, but they aren't because humans can still detect whether or not the thing is an LLM with a longer targetted conversation.
The final run is really giving the human with full knowledge of his task a full hour of investigating an LLM to decide whether it's human or a robot. If the LLM can deceive the human that is a hard True/False quantitative metric. That's really the only type of quantitative test left where there is a detectable difference.
I am still rather confused about how this fits into what you are saying more generally. At first I thought you were saying, in your latest post, that the Turing-test interrogator should be restricted to asking questions from the sets having quantitative metrics in order for it to be an objective process, but that doesn't really hold up, as far as I can see. Frankly, I suspect that the tests with objective metrics are beside the point, and the essence of your position is contained within your final paragraph: "If the LLM can deceive the human [then] that is a hard True/False quantitative metric [and the only sort we can get]."
If so, then (no surprise) I think there are some problems with it, but before I go further, I would like to check that I understand your position.
It matters because of humans. If I gave an LLM thousands of quantitative tests and it passed them all but in an hour long conversation a human could identify it was an LLM through some flaw the human would consider all those tests useless. That's why it matters. The human making a judgement call is still a quantitative measurement btw as you can limit human output to True or False. But because every human is different in order to get good numbers you have to do measurements with multitudes of humans.
>I am still rather confused about how this fits into what you are saying more generally. At first I thought you were saying, in your latest post, that the Turing-test interrogator should be restricted to asking questions from the sets having quantitative metrics in order for it to be an objective process, but that doesn't really hold up, as far as I can see.
it can still be objective with a human in the loop assuming the human is honest. What's not objective is a human offering an opinion in the form of a paragraph with no definitive clarity on what constitutes a metric. I realize that elements of MY metric have indeterminism to it, but it is still a hard metric because the output is over a well defined set. Whenever you have indeterminism you would then turn to probability and many samples in order to produce a final quantitative result.
>If so, then (no surprise) I think there are some problems with it, but before I go further, I would like to check that I understand your position.
yes my position is that exactly. If all observable qualities indicate it's a duck, then there's nothing more you can determine beyond that, scientifically speaking. You're implying there is a better way?
> It matters because of humans...
I'm still a bit puzzled here, because it seems to me that the paragraph continuing from here is making the argument that LLM performance on these tests doesn't matter, as far as the question is concerned: in this paragraph you seem to be saying (paraphrased) that despite LLMs' impressive performance on these quantitative tests, they could still fail Turing tests, so their performance on these quantitative tests is not decisive.
> yes my position is that exactly…
The impression I get from what you have written in this post is that you are not claiming that a test conforming to your requirements has actually been successfully performed, you are just assuming it could be?
Regardless, let’s assume (at least for the sake of argument) that the series of tests you propose have been performed, and the results are in: in the test environment, humans can’t distinguish current LLMs from humans any better than by chance. How do you get from that to answering the question we are actually interested in? The experiment does not explicitly address it. You might want to say something like “The Turing test has shown that the machines are as intelligent as humans so, like humans, these machines must realize that the language they receive is about an external world” but even the antecedent of that sentence is an interpretation that goes beyond what would have objectively been demonstrated by the Turing test, and the consequent is a subjective opinion that would not be entailed by the antecedent even if it were unassailable. Do you have a way to go from a successful Turing test to answering the question here, which meets your own quantitative and objective standards?
It matters in the quantitative sense. It measures AI performance. What it won't do is matter to YOU. Because you're a human and humans will keep moving the bar to a higher standard right? When AI shot passed the turing test humans just moved the goal posts. So to convince someone like YOU we have to look at the final metric. The point where LLM I/O becomes indistinguishable/superior to humans. Of course you look at the last decade... AI is rapidly approaching that final bar.
>The impression I get from what you have written in this post is that you are not claiming that a test conforming to your requirements has actually been successfully performed, you are just assuming it could be?
Whether I assume or don't assume, the projection of the trendline currently indicates that it will. Given the trendline that is the most probable conclusion.
>The experiment does not explicitly address it.
Nothing on the face of the earth can address the question. Because nobody truly knows what "understanding" something actually is. You can't even articulate the definition in a formal way such that it can be dictated on a computer program.
So I went to the next best possibility, which is my point. The point is ALTHOUGH we don't know what understanding is, we ALL assume humans understand things. So we set that as a bar metric. Anything indistinguishable from a human must understand things. Anything that appears close to a human but is not quite human must understand things ALMOST as well as a human.
It is disappointing to see you descending into something of a rant here. If you knew me better, you would know that I spend more time debating in opposition to people who think they can prove that AGI/artificial consciousness is impossible than I do with people who think it is already an undeniable fact that it has already been achieved (though this discussion is shifting the balance towards the middle, if only briefly.) Just because I approach arguments in either direction with a degree of skepticism and I don't see any value in trying to call the arrival of true AGI at the very first moment it occurs, it does not mean that I'm trying (whether secretly or openly) to deny that it is possible either in the near-term or at all. FWIW, I regard the former as possible and the latter highly probable, so long as we don't self-destruct first.
> Nothing on the face of the earth can address the question. Because nobody truly knows what "understanding" something actually is. You can't even articulate the definition in a formal way such that it can be dictated on a computer program.
The anti-AI folk I mentioned above would willingly embrace this position! They would say that it shows that human-like intelligence and consciousness lies outside of the scope of the physical sciences, and that this creates the possibility of a type of p-zombie that is indistinguishable by physical science from a human and yet lacks any concept of itself as an entity within an external world.
More relevantly, your response here repeats an earlier fallacy. In practice, concepts and their definitions are revised, tightened, remixed and refined as we inquire into them and gain knowledge. I know you don't agree, but as this is not an opinion but an empirical observation, validated by many cases in the history of science and science-like disciplines, I don't see you prevailing here - and there's the knowledge-bootstrap problem if this were not the case, as well.
It occurred to me this morning that there's a variant or extension of the quantitative Turing test which goes like this:
We have two agents and a judge. The judge is a human and the agents are either a pair of humans, a pair of AIs, or one of each, chosen randomly and without the judge being unaware of the mix. One of the agents is picked, by random choice, to start a discussion with the other with the intent of exploring what the other understands about some topic, with the discussion-starter being given the freedom to choose the topic. The discussion proceeds for a reasonable length of time - let's say one hour.
The judge follows the discussion but does not participate in it. At the conclusion of the discussion, the judge is required to say, for each agent, whether it is more likely that it is a human or AI, and the accuracy of this call is used to assign a categorical variable to the result, just as in the version of the Turing test you have described.
This seems just as quantitative, and in the same way, as your version, yet there's no reason to believe it will necessarily yield the same results. More tests are better, so what's not to like?
I'm going to be frank with you. I'm not ranting and uncharitable comments like this aren't appreciated. I'm going to respond to your reply later in another post, but if I see more stuff like this I'll stop stop communicating with you. Please don't say stuff like that.
That feeling is what sets the bar. There's no rhyme or reason behind it. But humans are the one who make the judgement call so that's what it has to be.
I will respond more later when I have time.
Yes I did say we can't define understanding. But despite the fact that we can't define it we still counter intuitively "know" when something has the capability of understanding. We say all humans have the capability of understanding.
This is the point. The word is undefined yet we can still apply the word and use the word and "know" whether something can understand things.
Thus we classify humans as capable of understanding things without any rhyme or reason. This is fine. But if you take this logic further, that means anything that is indistinguishable from a human must fit into this category.
That was my point. This is the logical limit of how far we can go with an undefined word. To be consistent with our logical application of the word "understanding" we must apply to AI if AI is indistinguishable from humans. If we don't do this then our reasoning is inconsistent. All of this can be done without even having a definition of the word "understanding"
Firstly, there have been a number of attempts to teach language to other animals, and also a persistent speculation that the complex vocalizations of bottlenose dolphins is a language. There is no consensus, however, on what to make of the results of the investigations, with different people offering widely disparate views as to the extent that these animals have, or have acquired language.
My take on these studies is that their language abilities are very limited at best, because they don't seem to grasp the power of language. They rarely initiate conversations, especially outside of a testing environment, and the conversations they do have are perfunctory. In the case of dolphins, if they had a well-developed language of their own, it seems unlikely that those being studied would fail to recognize that the humans they interact with have language themselves, and cooperate with the attempts of humans to establish communication, as this would have considerable benefit, such as being able to negotiate with the humans who exercise considerable control over their lives.
From these considerations, it seems to me that unless and until we see animals initiating meaningful conversations, especially between themselves without human prompting, it is pretty clear that their language skills do not match those of adult humans. This is what led me to see the value of a form of Turing test in which the test subjects demonstrate that they can initiate and sustain conversations.
A second consideration is that while human brains and minds are largely black boxes, we know a great deal about LLMs: humans designed them, they work as designed, and while they are not entirely deterministic, their stochastic aspect does not make their operation puzzling. We also know what they gain from their training: it is statistical information about token combinations in human language as it is actually used in the wild. It is not obvious that, from this, any entity could deduce that these token sequences often represent an external world that operates according to causes which are independent of what is said about the situation. An LLM is like a brain in a vat which only receives information in the form of a string of abstract tokens, without anything else to correlate it with, and it is incapable of interacting with the world to see how it responds.
From these considerations, therefore, it seems possible that, if LLMs understand anything, it is at most the structure of language as it is spoken or written, without being aware of an external world. I can't prove that this is so, but for the purpose of the arguments in this thread, and specifically the one in the first post that you replied to, all I need is that it is not ruled out.
Turning now to your latest post:
> For your test I don't see it offering anything new.
It is far from obvious that it will necessarily produce the same results as your test, and you have presented no argument that it will. If we are in the situation where one of these tests can discriminate between the candidate AIs and humans, then the only rational conclusion is that these candidate AIs can be distinguished from humans, even if the other test fails to do so.
> From a statistical point of view I feel it will yield roughly the same results as my test.
Throughout these conversations with me and other people, you have insisted that only quantitative tests are rigorous enough, but now you are arguing from nothing more than your opinion as to what the outcome would be. An opinion about what the quantitative results might be is not itself a quantitative result, and while you might be comfortable with the inconsistency of your position here, you can't expect the rest of us to agree.
> But despite the fact that we can't define [understanding] we still counter intuitively "know" when something has the capability of understanding. We say all humans have the capability of understanding... the word is undefined yet we can still apply the word and use the word and "know" whether something can understand things.
Good! This is a complete reversal from when you were arguing that understanding was not a valid concern unless it were rigorously defined.
> Thus we classify humans as capable of understanding things without any rhyme or reason. [my emphasis.]
If it were truly without rhyme or reason, 'understanding' would be an incoherent concept - a misconception or illusion. Fortunately, there is a rigorous way for handling this sort of thing: we can run a series of Turing-like tests, or simply one-on-one conversations, but only with human subjects, with multiple interrogators examining the same set of people and judging the extent to which they understand various test concepts. The degree of correlation between the outcomes will show us how coherent a concept it is, and the transcripts of the tests can be examined to begin the iterative process of defining what it is about the candidates that allows the judges to produce correlated judgements.
Once we have that in place, we can start adding AIs to the mix, confident that they are being judged by the same criteria as humans.
> But if you take this logic further, that means anything that is indistinguishable from a human must fit into this category.
Certainly not if the test is incapable of finding the distinction. The process I outlined above would be able to make the distinction, unless 'understanding' is not a coherent concept (but we seem to agree that it probably is.) Furthermore, as I pointed out above, one test capable of consistently making a distinction is all it takes.
There's no difference between doing something that works without understanding and doing the exact same thing with understanding.
The author is saying at best you can only set benchmark comparisons. We just assume all humans have the capability of understanding without even really defining the meaning of understanding. And if a machine can mimic human behavior to it must also understand.
That is literally how far we can go from a logical standpoint. It's the furthest we can go in terms of classifying things as either capable of understanding or not capable or close.
What you're not seeing is the LLM is not only mimicking human output to a high degree. It can even produce output that is superior to what humans can produce.
And no I did not say that. Let me be clear I did not say that there is "no difference". I said whether there is or isn't a difference we can't fully know because we can't define or know about what "understanding" is. At best we can only observe external reactions to input.
It does not seem that cultureswitch is an alias you are using, but even if it is, the above is unambiguously the claim I am referring to here, and no other.
As for the broader issues, we have already continued that discussion elsewhere: https://news.ycombinator.com/item?id=39503027
On writing that, I have an instinct to revise it to move the locus of understanding in the first example to the people who calculated the ballistic tables, based on physics first-principles. That would be more accurate, but my mistake highlights something interesting: an artillery officer / spotter simultaneously uses both. Is theirs a "deeper" / "truer" understanding? I don't think it is. I don't know what I think that means, for humans or AI.
I feel you are missing an important part of my point here. I am not taking a position on whether LLMs can be said to understand anything at all; I am saying that I seriously doubt that LLMs understand that the language they receive refers to an external world.
What is one such aspect? (I'm not asking in order to debate it here, but more because I want to test / research it on my own time)
Basically you just spend a lot of time with chatGPT4 and ask it deep questions that don't exist in it's dataset. get creative. The LLM will output answers that demonstrate a lack of understanding, but it will also demonstrate answers that display a remarkable amount of understanding. Both sets of answers exist and people often cite the wrong answers as evidence for lack of understanding but they're setting bar too high. The fact that many of these answers do demonstrate understanding of concepts makes it very very compelling.
Take for example Rock Paper Scissors.
https://chat.openai.com/share/ca22397c-2950-4919-bb79-6def64...
This entire conversation thread I believe does not exist in a parallel form in it's data set. It demonstrates understanding of RPS beyond the confines of text, it demonstrates understanding of simultaneity EVEN when the LLM wholly lives in a world of turn based questions and responses, it understands itself relative to simultaneity, it tries to find solutions around it's own problem, it understands how to use creativity and solutions such as cryptography to solve the problem of RPS when playing with it, it also understands the weaknesses of it's own solutions.
Conversations such as this show that chatGPT displays remarkable understanding of the world. There are conversations that are opposite to this that demonstrate LLMs displaying an obvious lack of understanding. But the existence of these conversation that lack understanding does NOT negate the ones that do demonstrate understanding. The fact that partial understanding even exists is a milestone for AI.
This isn't Anthropomorphism. People are throwing this word trying to get people to recognize their own biases without realizing that it's just demonstrating their own biases. We literally can't even define "understanding" and both LLMs and the human brain are black boxes. Making adamant claims saying that LLMs don't understand anything without addressing this fact is itself a form of bias.
The way I address the problem above is that I just define a bar. I define humans as the bar of "understanding" without defining what understanding means itself. Then if any machine begins approaching this bar in terms of input and output matching human responses, then this is logically indistinguishable from approaching "understanding". That's literally the best metric we have.
I don't know that that is quite the right question to ask.
Understanding exists on a spectrum. Even humans don't necessarily understand everything they say or claim (incl. what they say of LLMs!), and then there are things a particular human would simply say "I don't understand".
But when you ask a human "can you understand things?" you will get an unequivocal Yes!
Ask that same question of an LLM and what does it say? I don't think any of them currently respond with a simple or even qualified "Yes". Now, some might claim that one day an LLM will cross that threshold and say "Yes!" but we can safely leave that off to the side for a future debate if it ever happens.
General note: it is worth separating out things like "understanding", "knowledge", "intelligence", "common sense", "wisdom", "critical thinking", etc. While they might all be related in some ways and even overlap, it does not follow that if you show high performance in one that you automatically excel in each of the other. I know many people who anyone would say are highly intelligent but lack common sense, etc.
People in particular have evolved complex self protective mechanisms to provide the right answers for their given environment for safety reasons, based on a number of different individual strategies. For example, the overly honest, the self depreciating, the questioner, the prosecutor, the victim, the liar, the absent minded professor, the idiot, etc.
LLMs are not that complex or self-referential.
Personally, my guess is that you'd want to build a model (of some kind!) whose sole job is determining the credibility of given string of tokens (similar to what someone else noted in a sibling comment about high answer volatility based on minor input changes - that does sound like a signal of low credibility), and somehow integrate THAT self-referential feedback into the process.
Notably, even the smartest lawyers (or perhaps, especially the smartest lawyers) will have assistants do research once they've set out a strategy so they are sure THEY aren't bullshitting. Same with professors, professional researchers, engineers, etc.
Because until someone goes and actually reads the case law from a credible source, or checks the primary research, or calculates things, it's possible someone was misremembering or just wrong.
Being right more often is not about never having a wrong thought/idea/statement, it's about double checking when you're thinking you might be bullshitting, and NOT saying the bullshit answer until you've checked. Which is proportionally, very expensive. The really good professionals will generate MANY lines of such inquiry in parallel for folks to track down, and then based on their degree of confidence in each one and the expected context the answer will be used in, will formulate the 'most correct' response, which is proportionally even more expensive.
So at least during the process, there would be a signal that the system was likely 'bullshitting'. Which might help it in at least being able to signal when it's answers are low-confidence. (human equivalent of stuttering, looking down and away, looking ashamed haha!)
Every human gets fooled sometimes in at least some venue though.
That's certainly one root of the problem, but I would argue that there are multiple roots to this problem!
Humans have further realized that understanding itself is provisional and incomplete, which is quite a remarkable insight (understanding if you will), itself.
http://v.cx/2010/04/feynman-brazil-education
The students learned to repeat the text of the books, without "understanding" what the books were describing. I'm sure this says something about one side or the other of this conundrum, but I'm not sure which. :-)
The central claim is that a machine which answers exactly the same thing a human would answer given the same input does not have understanding, while the human does.
This claim is religious, not scientific. In this worldview, "understanding" is a property of humans which can't be observed but exists nonetheless. It's like claiming humans have a soul.
People also often don't understand things and have trouble separating fact from fiction. By logic only one religion or no religion is true. Consequently also by logic most religions in the world where their followers believe the religion to be true are hallucinating.
The second thing to realize that your argument doesn't really apply. Its in theory possible to create a stochastic parrot that can imitate to a degree of 100 percent the output of a human who truly understands things. It blurs the line of what is understanding.
One can even define true understanding as a stochastic parrot that generated text indistinguishable total understanding.
That's not the point being argued. Understanding, critical thinking, knowledge, common sense, etc. all these things exist on a spectrum - both in principle and certainly in humans. In fact, in any particular human there are different levels of competence across these dimensions.
What we are debating, is whether or not, an LLM can have understanding itself. One test is: can an LLM understand understanding? The human mind has come to the remarkable understanding that understanding itself is provisional and incomplete.
In fact. That question is one of the more trivial questions it will most likely not hallucinate on.
The reason why I alluded to humans here is because I'm saying we are setting the bar too high. It's like everyone is saying it hallucinates and therefore it can't understand anything. I'm saying that we hallucinate too and because of that LLMs can approach humans and human level understanding.