it’s only going to get worse, especially as datasets deteriorate.
With things like reddit being overrun by AI, and also selling AI training data, i can only imagine what mess that’s going to cause.
Hallucinations, like depression, is a multifaceted issue. Training data is only a piece of it. Quantized models, overfitted training models rely on memory at the cost of obviously correct training data. Poorly structured Inferences can confuse a model.
Rest assured, this isn’t just training data.
yeah there’s also this stuff as well, though i consider that to be a more technical challenge, rather than a hard limit.
We not only have to stop ignoring the problem, we need to be absolutely clear about what the problem is.
LLMs don’t hallucinate wrong answers. They hallucinate all answers. Some of those answers will happen to be right.
If this sounds like nitpicking or quibbling over verbiage, it’s not. This is really, really important to understand. LLMs exist within a hallucinatory false reality. They do not have any comprehension of the truth or untruth of what they are saying, and this means that when they say things that are true, they do not understand why those things are true.
That is the part that’s crucial to understand. A really simple test of this problem is to ask ChatGPT to back up an answer with sources. It fundamentally cannot do it, because it has no ability to actually comprehend and correlate factual information in that way. This means, for example, that AI is incapable of assessing the potential veracity of the information it gives you. A human can say “That’s a little outside of my area of expertise,” but an LLM cannot. It can only be coded with hard blocks in response to certain keywords to cut it from answering and insert a stock response.
This distinction, that AI is always hallucinating, is important because of stuff like this:
But notice how Reid said there was a balance? That’s because a lot of AI researchers don’t actually think hallucinations can be solved. A study out of the National University of Singapore suggested that hallucinations are an inevitable outcome of all large language models. **Just as no person is 100 percent right all the time, neither are these computers. **
That is some fucking toxic shit right there. Treating the fallibility of LLMs as analogous to the fallibility of humans is a huge, huge false equivalence. Humans can be wrong, but we’re wrong in ways that allow us the capacity to grow and learn. Even when we are wrong about things, we can often learn from how we are wrong. There’s a structure to how humans learn and process information that allows us to interrogate our failures and adjust for them.
When an LLM is wrong, we just have to force it to keep rolling the dice until it’s right. It cannot explain its reasoning. It cannot provide proof of work. I work in a field where I often have to direct the efforts of people who know more about specific subjects than I do, and part of how you do that is you get people to explain their reasoning, and you go back and forth testing propositions and arguments with them. You say “I want this, what are the specific challenges involved in doing it?” They tell you it’s really hard, you ask them why. They break things down for you, and together you find solutions. With an LLM, if you ask it why something works the way it does, it will commit to the bit and proceed to hallucinate false facts and false premises to support its false answer, because it’s not operating in the same reality you are, nor does it have any conception of reality in the first place.
This right here is also the reason why AI fanboys get angry when they are told that LLMs are not intelligent or even thinking at all. They don’t understand that in order for rational intelligence to exist, the LLMs should be able to have an internal, referential inner world of symbols, to contrast external input (training data) against and that is also capable of changing and molding to reality and truth criteria. No, tokens are not what I’m talking about. I’m talking about an internally consistent and persistent representation of the world. An identity, which is currently antithetical with the information model used to train LLMs. Let me try to illustrate.
I don’t remember the details or technical terms but essentially, animal intelligence needs to experience a lot of things first hand in order to create an individualized model of the world which is used to direct behavior (language is just one form of behavior after all). This is very slow and labor intensive, but it means that animals are extremely good, when they get good, at adapting said skills to a messy reality. LLMs are transactional, they rely entirely on the correlation of patterns of input to itself. As a result they don’t need years of experience, like humans for example, to develop skilled intelligent responses. They can do it in hours of sensing training input instead. But at the same time, they can never be certain of their results, and when faced with reality, they crumble because it’s harder for it to adapt intelligently and effectively to the mess of reality.
LLMs are a solipsism experiment. A child is locked in a dark cave with nothing but a dim light and millions of pages of text, assume immortality and no need for food or water. As there is nothing else to do but look at the text they eventually develop the ability to understand how the symbols marked on the text relate to each other, how they are usually and typically assembled one next to the other. One day, a slit on a wall opens and the person receives a piece of paper with a prompt, a pencil and a blank page. Out of boredom, the person looks at the prompt, it recognizes the symbols and the pattern, and starts assembling the symbols on the blank page with the pencil. They are just trying to continue from the prompt what they think would typically follow or should follow afterwards. The slit in the wall opens again, and the person intuitively pushes the paper it just wrote into the slit.
For the people outside the cave, leaving prompts and receiving the novel piece of paper, it would look like an intelligent linguistic construction, it is grammatically correct, the sentences are correctly punctuated and structured. The words even make sense and it says intelligent things in accordance to the training text left inside and the prompt given. But once in a while it seems to hallucinate weird passages. They miss the point that, it is not hallucinating, it just has no sense of reality. Their reality is just the text. When the cave is opened and the person trapped inside is left into the light of the world, it would still be profoundly ignorant about it. When given the word sun, written on a piece of paper, they would have no idea that the word refers to the bright burning ball of gas above them. It would know the word, it would know how it is usually used to assemble text next to other words. But it won’t know what it is.
LLMs are just like that, they just aren’t actually intelligent as the person in this mental experiment. Because there’s no way, currently, for these LLMs to actually sense and correlate the real world, or several sources of sensors into a mentalese internal model. This is currently the crux and the biggest problem on the field of AI as I understand it.
How do hallucinations preclude an internal representation? Couldn’t hallucinations arise from a consistent internal representation that is not fully aligned with reality?
I think you are misunderstanding the role of tokens in LLMs and conflating them with internal representation. Tokens are used to generate a state, similar to external stimuli. The internal representation, assuming there is one, is the manner in which the tokens are processed. You could say the same thing about human minds, that the representation is not located anywhere like a piece of data; it is the manner in which we process stimuli.
Not really. Reality is mostly a social construction. If there’s not an other to check and bring about meaning, there is no reality, and therefore no hallucinations. More precisely, everything is a hallucination. As we cannot cross reference reality with LLMs and it cannot correct itself to conform to our reality. It will always hallucinate and it will only coincide with our reality by chance.
I’m not conflating tokens with anything, I explicitly said they aren’t an internal representation. They’re state and nothing else. LLMs don’t have an internal representation of reality. And they probably can’t given their current way of working.
You seem pretty confident that LLMs cannot have an internal representation simply because you cannot imagine how that capability could emerge from their architecture. Yet we have the same fundamental problem with the human brain and have no problem asserting that humans are capable of internal representation. LLMs adhere to grammar rules, present information with a logical flow, express relationships between different concepts. Is this not evidence of, at the very least, an internal representation of grammar?
We take in external stimuli and peform billions of operations on them. This is internal representation. An LLM takes in external stimuli and performs billions of operations on them. But the latter is incapable of internal representation?
And I don’t buy the idea that hallucinations are evidence that there is no internal representation. We hallucinate. An internal representation does not need to be “correct” to exist.
Yet we have the same fundamental problem with the human brain
And LLMs aren’t human brains, they don’t even work remotely similarly. An LLM has more in common with an Excel spreadsheet than with a neuron. Read on the learning models and pattern recognition theories behind LLMs, they are explicitly designed to not function like humans. So we cannot assume that the same emergent properties exist on an LLM.
Nor can we assume that they cannot have the same emergent properties.
That’s an excellent methaphor for LLMs.
It’s the Chinese room thought experiment.
Hadn’t heard about it before (or maybe I did but never looked into it), so I just went and found it in Wikipedia and will be reading all about it.
So thanks for the info!
No worries. The person above did a good job explaining it although they kind of mashed it together with the imagery from Plato’s allegory of the cave.
Wtf are you even talking about.
They are right though. LLM at their core are just about determining what is statistically the most probable to spit out.
Your 1 sentence makes more sense than the slop above.
they do not understand why those things are true.
Some researchers compared the results of questions between chat gpt 3 and 4. One of the questions was about stacking items in a stable way. Chat gpt 3 just, in line with what you are saying about “without understanding”, listed the items saying to place them one on top of each other. No way it would have worked.
Chat gpt 4, however, said that you should put the book down first, put the eggs in a 3 x 3 grid on top of the book, trap them in a way with a laptop so they don’t roll around, and then put the bottle on top of the laptop standing up, and then balance the nail on the top of it…even noting you have to put the flat end of the nail down. This sounds a lot like understanding to me and not just rolling the dice hoping to be correct.
Yes, AI confidently gets stuff wrong. But let’s all note that there is a whole subreddit dedicated to people being confidently wrong. One doesn’t need to go any further than Lemmy to see people confidently claiming to know the truth about shit they should know is outside of their actual knowledge. We’re all guilty of this. Including refusing to learn when we are wrong. Additionally, the argument that they can’t learn doesn’t make sense because models have definitely become better.
Now I’m not saying ai is conscious, I really don’t know, but all of your shortcomings you’ve listed humans are guilty of too. So to use it as examples as to why it’s always just a hallucination, or that our thoughts are not, doesn’t seem to hold much water to me.
the argument that they can’t learn doesn’t make sense because models have definitely become better.
They have to be either trained with new data or their internal structure has to be improved. It’s an offline process, meaning they don’t learn through chat sessions we have with them (if you open a new session it will have forgotten what you told it in a previous session), and they can’t learn through any kind of self-directed research process like a human can.
all of your shortcomings you’ve listed humans are guilty of too.
LLMs are sophisticated word generators. They don’t think or understand in any way, full stop. This is really important to understand about them.
They have to be either trained with new data or their internal structure has to be improved. It’s an offline process, meaning they don’t learn through chat sessions we have with them (if you open a new session it will have forgotten what you told it in a previous session), and they can’t learn through any kind of self-directed research process like a human can.
Most human training is done through the guidance of another, additionally, most of this is training is done through an automated process where some computer is just churning through data. And while you are correct that the context does not exist from one session to the next, you can in fact teach it something and it will maintain it during the session. It’s just like moving to a new session is like talking to completely different person, and you’re basically arguing “well, I explained this one thing to another human, and this human doesn’t know it. . .so how can you claim it’s thinking?” And just imagine the disaster that would happen if you would just allow it to be trained by anyone on the web. It would be spitting out memes, racism, and right wing propaganda within days. lol
They don’t think or understand in any way, full stop.
I just gave you an example where this appears to be untrue. There is something that looks like understanding going on. Maybe it’s not, I’m not claiming to know, but I have not seen a convincing argument as to why. Saying “full stop” instead of an actual argument as to why just indicates to me that you are really saying “stop thinking.” And I apologize but that’s not how I roll.
Most human training is done through the guidance of another
Let’s take a step back and not talk about training at all, but about spontaneous learning. A baby learns about the world around it by experiencing things with its senses. They learn a language, for example, simply by hearing it and making connections - getting corrected when they’re wrong, yes, but they are not trained in language until they’ve already learned to speak it. And once they are taught how to read, they can then explore the world through signs, books, the internet, etc. in a way that is often self-directed. More than that, humans are learning at every moment as they interact with the world around them and with the written word.
An LLM is a static model created through exposure to lots and lots of text. It is trained and then used. To add to the model requires an offline training process, which produces a new version of the model that can then be interacted with.
you can in fact teach it something and it will maintain it during the session
It’s still not learning anything. LLMs have what’s known as a context window that is used to augment the model for a given session. It’s still just text that is used as part of the response process.
They don’t think or understand in any way, full stop.
I just gave you an example where this appears to be untrue. There is something that looks like understanding going on.
You seem to have ignored the preceding sentence: “LLMs are sophisticated word generators.” This is the crux of the matter. They simply do not think, much less understand. They are simply taking the text of your prompts (and the text from the context window) and generating more text that is likely to be relevant. Sentences are generated word-by-word using complex math (heavy on linear algebra and probability) where the generation of each new word takes into account everything that came before it, including the previous words in the sentence it’s a part of. There is no thinking or understanding whatsoever.
This is why Voroxpete@sh.itjust.works said in the original post to this thread, “They hallucinate all answers. Some of those answers will happen to be right.” LLMs have no way of knowing if any of the text they generate is accurate for the simple fact that they don’t know anything at all. They have no capacity for knowledge, understanding, thought, or reasoning. Their models are simply complex networks of words that are able to generate more words, usually in a way that is useful to us. But often, as the hallucination problem shows, in ways that are completely useless and even harmful.
You are just wrong
usually, what I see is that the REPL they are using is never introspective enough. The ai cant on its own revert to a prevous state or give notes to itself because the response being fast and in linear time matters for a chatbot. ChatGPT can make really cool stuff when you ask it to break it’s thoght process into steps. Ones it usually fails spectacularly at. It was like pulling teeth to get it to actually do the steps and not just give the bad answer anyway.
They do not have any comprehension of the truth or untruth of what they are saying, and this means that when they say things that are true, they do not understand why those things are true.
Which can be beautifully exploited with sponsored content.
See Google I/O '24.
What specifically in Google I/O?
Alternative title for this year Google I/O: AI vomit. You can watch Verge’s TL;DW video on Google I/O. There is no panel that did not mention AI. Most of it is “user centric”.
AI can deliver and gather ad data. The bread and butter for Google.
As to how it relates to the quote. It is up to Google to make it as truthful as they want it to be. And given ads is their money driver.
I fucking hate how OpenAi and other such companies claim their models “understand” language or are “fluent” in French. These are human attributes. Unless they made a synthetic brain, they can take these claims and shove them up their square tight corporate behinds.
This is circular logic: only humans can be fluent, so the models can’t be fluent because they aren’t human.
And it’s universally upvoted…in response to an ais getting things wrong so they can’t be doing anything but hallucinating.
And will you learn from this? Nope. I’ll just be down voted and shouted at.
It’s not circular. LLMs cannot be fluent because fluency comes from an understanding of the language. An LLM is incapable of understanding so it is incapable of being fluent. It may be able to mimic it but that is a different thing. (In my opinion)
You might agree with the conclusion, and the conclusion might even be correct, but the poster effectively argued ‘only humans can be fluent, and it’s not a human so it isn’t fluent’ and that is absolutely circular logic.
This is not at all what I said. If a machine was complex enough to reason, all power to it. But these LLMs cannot.
I think where you are going wrong here is assuming that our internal perception is not also a hallucination by your definition. It absolutely is. But our minds are embodied, thus we are able check these hallucinations against some outside stimulus. Your gripe that current LLMs are unable to do that is really a criticism of the current implementations of AI, which are trained on some data, frozen, then restricted from further learning by design. Imagine if your mind was removed from all stimulus and then tested. That is what current LLMs are, and I doubt we could expect a human mind to behave much better in such a scenario. Just look at what happens to people cut off from social stimulus; their mental capacities degrade rapidly and that is just one type of stimulus.
Another problem with your analysis is that you expect the AI to do something that humans cannot do: cite sources without an external reference. Go ahead right now and from memory cite some source for something you know. Do not Google search, just remember where you got that knowledge. Now who is the one that cannot cite sources? The way we cite sources generally requires access to the source at that moment. Current LLMs do not have that by design. Once again, this is a gripe with implementation of a very new technology.
The main problem I have with so many of these “AI isn’t really able to…” arguments is that no one is offering a rigorous definition of knowledge, understanding, introspection, etc in a way that can be measured and tested. Further, we just assume that humans are able to do all these things without any tests to see if we can. Don’t even get me started on the free will vs illusory free will debate that remains unsettled after centuries. But the crux of many of these arguments is the assumption that humans can do it and are somehow uniquely able to do it. We had these same debates about levels of intelligence in animals long ago, and we found that there really isn’t any intelligent capability that is uniquely human.
This seems to be a really long way of saying that you agree that current LLMs hallucinate all the time.
I’m not sure that the ability to change in response to new data would necessarily be enough. They cannot form hypotheses and, even if they could, they have no way to test them.
My thesis is that we are asserting the lack of human-like qualities in AIs that we cannot define or measure. Assertions should be made on data, not uneasy feelings arising when an LLM falls into the uncanny valley.
But we do know how they operate. I saw a post a while back where somebody asked the LLM how it was calculating (incorrectly) the date of Easter. It answered with the formula for the date of Easter. The only problem is that that was a lie. It doesn’t calculate. You or I can perform long multiplication if asked to, but the LLM can’t (ironically, since the hardware it runs on is far better at multiplication than we are).
We do not know how LLMs operate. Similar to our own minds, we understand some primitives, but we have no idea how certain phenomenon emerge from those primitives. Your assertion would be like saying we understand consciousness because we know the structure of a neuron.
Holy shit. Dunning Kruger is fully engaged in these post comments
It will never be solved. Even the greatest hypothetical super intelligence is limited by what it can observe and process. Omniscience doesn’t exist in the physical world. Humans hallucinate too - all the time. It’s just that our approximations are usually correct, and then we don’t call it a hallucination anymore. But realistically, the signals coming from our feet take longer to process than those from our eyes, so our brain has to predict information to create the experience. It’s also why we don’t notice our blinks, or why we don’t see the blind spot our eyes have.
AI representing a more primitive version of our brains will hallucinate far more, especially because it cannot verify anything in the real world and is limited by the data it has been given, which it has to treat as ultimate truth. The mistake was trying to turn AI into a source of truth.
Hallucinations shouldn’t be treated like a bug. They are a feature - just not one the big tech companies wanted.
When humans hallucinate on purpose (and not due to illness), we get imagination and dreams; fuel for fiction, but not for reality.
You assume the physical world is all there is or that the AI has any real intelligence at all. It’s a damn chinese room.
I think you’re giving a glorified encyclopedia too much credit. The difference between us and “AI” is that we can approach knowledge from a problem solving position. We do approximate the laws of physics, but we don’t blindly take our beliefs and run with it. We put we come up with a theory that then gets rigorously criticized, then come up with ways to test that theory, then be critical of the test results and eventually we come to consensus that based on our understandings that thing is true. We’ve built entire frameworks to reduce our “hallucinations”. The reason we even know we have blind spots is because we’re so critical of our own “hallucinations” that we end up deliberately looking for our blind spots.
But the “AI” doesn’t do that. It can’t do that. The “AI” can’t solve problems, it can’t be critical of itself or what information its giving out. All our current “AI” can do is word vomit itself into a reasonable answer. Sometimes the word vomit is factually correct, sometimes it’s just nonsense.
You are right that theoretically hallucinations cannot be solved, but in practicality we ourselves have come up with solutions to minimize it. We could probably do something similar with “AI” but not when the AI is just a LLM that fumbles into sentences.
I’m not sure where you think I’m giving it too much credit, because as far as I read it we already totally agree lol. You’re right, methods exist to diminish the effect of hallucinations. That’s what the scientific method is. Current AI has no physical body and can’t run experiments to verify objective reality. It can’t fact check itself other than be told by the humans training it what is correct (and humans are fallible), and even then if it has gaps in what it knows it will fill it up with something probable - but which is likely going to be bullshit.
All my point was, is that to truly fix it would be to basically create an omniscient being, which cannot exist in our physical world. It will always have to make some assumptions - just like we do.
The fundamental difference is that the AI doesn’t know anything. It isn’t capable of understanding, it doesn’t learn in the same sense that humans learn. A LLM is a (complex!) digital machine that guesses the next most likely word based on essentially statistics, nothing more, nothing less.
It doesn’t know what it’s saying, nor does it understand the subject matter, or what a human is, or what a hallucination is or why it has them. They are fundamentally incapable of even perceiving the problem, because they do not perceive anything aside from text in and text out.
Very long layman take. Why is there always so many of these on every ai post? What do you get from guesstimating how the technology works?
I’m not an expert in AI, I will admit. But I’m not a layman either. We’re all anonymous on here anyways. Why not leave a comment explaining what you disagree with?
I want to just understand why people get so passionate about explaining how things work, especially in this field where even the experts themselves just don’t understand how it works? It’s just an interesting phenomenon to me
What exactly are your bona fides that you get to play the part of the exasperated “expert” here? And, more importantly, why should I give a fuck?
I constantly hear this shit from other self-appointed experts in this field as if no one is allowed to discuss, criticize, or form opinions on the implications of this technology besides those few who ‘truly understand’.
Did you misread something? Nothing of what you said is relevant
The not understanding hlw it works thing isn’t universal in ai from my understanding. And people understand how a lot of it works even then. There may be a few mysterious but its not sacrificing chickens to Jupiter either.
Nope, it’s actually not understood. Sorry to hear you don’t understand that
I’m a bit annoyed at all the people being pedantic about the term hallucinate.
Programmers use preexisting concepts as allegory for computer concepts all the time.
Your file isn’t really a file, your desktop isn’t a desk, your recycling bin isn’t a recycling bin.
[Insert the entirety of Object Oriented Programming here]
Neural networks aren’t really neurons, genetic algorithms isn’t really genetics, and the LLM isn’t really hallucinating.
But it easily conveys what the bug is. It only personifies the LLM because the English language almost always personifies the subject. The moment you apply a verb on an object you imply it performed an action, unless you limit yourself to esoteric words/acronyms or you use several words to overexplain everytime.
What I don’t like about it is that it makes it sound more benign than it is. Which also points to who decided to use that term - AI promoters/proponents.
Edit: it’s like all of the bills/acts in congress where they name them something like “The Protect Children Online Act” and you ask, “well, what does it do?” And they say something like, “it lets local police read all of your messages so they can look for any dangers to children.”
The term “hallucination” has been used for years in AI/ML academia. I reading about AI hallucinations ten years ago when I was in college. The term was originally coined by researchers and mathematicians, not the snake oil salesman pushing AI today.
I had no idea about this. I studied neural networks briefly over 10 years ago, but hadn’t heard the term until the last year or two.
We were talking about when it was coined, not when you heard it first
In terms of LLM hallucination, it feels like the name very aptly describes the behavior and severity. It doesn’t downplay what’s happening because it’s generally accepted that having a source of information hallucinate is bad.
I feel like the alternatives would downplay the problem. A “glitch” is generic and common, “lying” is just inaccurate since that implies intent to deceive, and just being “wrong” doesn’t get across how elaborately wrong an LLM can be.
Hallucination fits pretty well and is also pretty evocative. I doubt that AI promoters want to effectively call their product schizophrenic, which is what most people think when hearing hallucination.
Ultmately all the sciences are full of analogous names to make conversations easier, it’s not always marketing. No different than when physicists say particles have “spin” or “color” or that spacetime is a “fabric” or [insert entirety of String theory]…
After thinking about it more, I think the main issue I have with it is that it sort of anthropomorphises the AI, which is more of an issue in applications where you’re trying to convince the consumer that the product is actually intelligent. (Edit: in the human sense of intelligence rather than what we’ve seen associated with technology in the past.)
You may be right that people could have a negative view of the word “hallucination”. I don’t personally think of schizophrenia, but I don’t know what the majority think of when they hear the word.
You could invent a new word, but that doesn’t help people understand the problem.
You are looking for an existing word that describes providing unintentionally incorrect thoughts but is totally unrelated to humans. I suspect that word doesn’t exist. Every thinking word gets anthropomorphized.
