It is a very basic fact that LLMs have no concept of true or false, it only has an ability to look up what text data it has seen before. If you do not understand this you are in no position to discuss LLMs.
The ~100 layer deep neural networks infer many levels of features over the text, including the concept of true and false. That is trivial for an LLM.
Are you completely unaware these are based on deep neural networks?
Convolutional Neural Networks don't operate by "look up" of text data.
It's response is not based on facts about the world as it exists, but on the text data it has been trained on. As such, it is not able to determine true or false even if the response in the above example would be correct.
I know I don't experience the world as it is, but rather through a whole bunch of different signals I get that give me some hints about what the real world might be. For example, text.
LLM does not, that isn't how it works.
You can say the difference is academic but there is a difference.
What is the difference between a real good faker of intelligence and actual intelligence is an open question.
But I will say most AI experts agree that LLM are not artificial general intelligence. It isn't just a lack of training data, they just are not of the category that we mean by that.
> LLM does not, that isn't how it works.
GPT-4 can explain the concept when prompted and can evaluate logic problems better than most human beings can. I would say it has a deeper understanding of "true vs false" than most humans.
I think what you are trying to say is that LLMs are not conscious. Consciousness has no precise universally agreed formal definition, but we all know that LLMs are not conscious.
Sigh
GPT produces output which obeys the patterns it has been trained on for definitions of true and false. It does not understand anything. It is a token manipulation machine. It does it well enough that it convinces you, a walking ape, that it understands. It does not.
You have a subjective philosophical disagreement about what constitutes understanding. That is fine. I clearly understand it is not conscious and that programs do not understand things the way that humans do. We are fundamentally different to LLMs. That is obvious. But you are not making a technical argument here unless you can define "understand" in technical terms. This is a matter of semantics.
> It is a token manipulation machine
Deep learning and machine learning in general is more than token manipulation. They are designed for pattern recognition.
The broad strokes you use here are exactly why discussing LLMs are hard. Sure some people dismiss them because it isn't general AI but having supporters dismiss any argument with "passes the Turning test" is equally useless.
"But you are not making a technical argument here unless you can define "understand" in technical terms. This is a matter of semantics."
I said the nature of their argument is not technical, since they are not dealing with technical definitions, but I did not dismiss their argument altogether. I clarified and restated their own argument for them in clearer terms. LLMs are not conscious, but they can still "understand" very well depending on your definition of understand. Understanding is not a synonym for consciousness. Language is evolving and you need to be more precise when discussing AI / machine learning.
One definition of understand is:
"perceive the intended meaning of (words, a language, or a speaker)."
Deep learning models recognize patterns. Mechanical perception of patterns. They understand things mechanically, unconsciously.
The core issue is their "knowledge" is too context sensitive.
Certainly humans are very context sensitive in our memories but we all have something akin to a "mental model" we can use to find things without that context.
In contrast LLM has knowledge defined by that context quite literally.
In either case my original point on using true and false is that LLM can hallucinate and on a fundamental design level there is little that can be done to stop it.
https://en.wikipedia.org/wiki/Natural-language_understanding
My point was to show that their thinking, reasoning and language was flawed, that it lacked nuance and rigor. I am trying to raise the standards of discussion. They need to think more deeply about what "understanding" really means. Consciousness does not even have a formal universally agreed definition.
Sloppy non-rigorous shallow arguments are bad for discussion.
> LLM can hallucinate and on a fundamental design level there is little that can be done to stop it.
That's a separate issue. They generally don't hallucinate when solving a problem within their context window. Recalling facts from their training set is another issue.
Humans sometimes have a similar problem of "hallucinating" when recalling facts from their long term memory.
GPT-3.5 was trained on at least 300 billion tokens. It has 96 layers in its neural network of 175 billion parameters. Each one of those 96 stacked layers has an attention mechanism that recomputes an attention score for every token in the context window, for each new token generated in sequence. GPT-4 is much bigger than that. The scale and complexity of these models is beyond comprehension. We're talking about LLMs, not SLMs.
ChatGPT can tell me about itself when prompted. It tells me that it is an LLM. It can tell me about capabilities and limitations. It can describe the algorithms that generate itself. It has deep self knowledge, but is not conscious.
Your eyes and ears perceive a tiny minuscule fraction of what is out there in the real world.
A blind and deaf person must know even less of the real world than an LLM, which can read more than a human can ever read in their lifetime.
How did you find out that Biden was elected if not through language by reading or listening to news? Do you have extra sensory perception? Psychic powers? Do you magically perceive "facts" without any sensory input or communication? Ridiculous.
By the same argument your knowledge is also not based on "facts" about the world, since you only learned about it by reading or listening. Absurd nonsense.
Do LLMs know true or false? It depends on how you define "know". By some definitions, they "know true or false" better than humans, as they can explain the concept and solve logic problems better than most humans can. However, by any definition that requires consciousness, they do not know because they are not conscious.
The average person spends a lot of time completely immersed in "false" entertainment. Actors are all liars, pretending to be someone they are not, doing things that didn't really happen, and yet many people are convinced it is all "true" for at least a few minutes.
People also believe crazy things like Flat Earth theory or that the Apollo moon landings were faked.
So LLMs have a conceptual understanding of true/false, strong logical problem solving to evaluate truth or falsity of logical statements, and factual understanding of what is true and false, better than many humans do. But they are not conscious therefore they are not conscious of what is true or false.
And I have no idea what you mean by saying that it has no concept of true or false. Even the simplest computer programs have a concept of true or false, that's kind of the simplest data type, a boolean. Large language models have a much more sophisticated concept of true and false that has a lot more nuance. That's really a pretty ridiculous thing to say.
Tell me how that doesn't apply to the human brain as well.