> It can just parrot the collective understanding humans already have and teach it.
The problem with calling an LLM a parrot is that anyone who has actually interacted with an LLM knows that it produces completely novel responses to questions it has never seen before. These answers are usually logical and reasonable, based on both the information you gave the LLM and its previous knowledge of the world. Doing that requires understanding.
> They never make new connections that aren't in training data.
This is just categorically untrue. They make all sorts of logical connections that are not explicitly contained in the training data. Making logical inferences about subjects one has never heard about - based on the things one does know - is an expression of understanding. LLMs do that.
You claim that logical and reasonable responses "require understanding" therefore LLMs must understand. But I see LLMs as evidence that understanding is not required to produce logical and reasonable responses.
Thinking back to when I used to help tutor some of my peers in 101-level math classes there were many times someone was able to produce a logical and reasonable response to a problem (by rote use of an algorithm) but upon deeper interrogation it became clear that they lacked true understanding.
A human that mimics the speech of someone that does understand usually doesn't understand himself. We see that happen all the time with real humans, you have probably seen that as well.
To see if a human understands we ask them edge questions and things they probably haven't seen before, and if they fail there but just manage for common things then we know the human just faked understanding. Every LLM today fails this, so they don't understand, just like we say humans don't understand that produces the same output. These LLM has superhuman memory so their ability to mimic smart humans is much greater than a human faker, but other than that they are just like your typical human faker.
> A human that mimics the speech of someone that does understand usually doesn't understand himself.
That's not what LLMs do. They provide novel answers to questions they've never seen before, even on topics they've never heard of, that the user just made up.
> To see if a human understands we ask them edge questions
This is testing if there are flaws in their understanding. My dog understands a lot of things about the world, but he sometimes shows that he doesn't understand basic things, in ways that are completely baffling to me. Should I just throw my hands in the air and declare that dogs are incapable of understanding anything?
Isn't this describing temperature induced randomness and ascribing some kind of intelligence to it? This assertion has been made and refuted multiple times on this thread and no solid evidence to the contrary presented.
To go back to your first sentence - interacting with an llm is not understanding how it works, building one is. The actual construction of a neural network llm refutes your assertions.
The claim was made that LLMs just parrot back what they've seen in the training data. They clearly go far beyond this and generate completely novel ideas that are not in the training data. I can give ChatGPT extremely specific and weird prompts that have 0% chance of being in its training data, and it will answer intelligently.
> The actual construction of a neural network llm refutes your assertions.
I don't see how. There's a common view that I see expressed in these discussions, that if the workings of an LLM can be explained in a technical manner, then it doesn't understand. "It just uses temperature induced randomness, etc. etc." Once we understand how the human brain works, it will then be possible to argue, in the exact same way, that humans do not understand. "You see, the brain is just mechanically doing XYZ, leading to the vocal cords moving in this particular pattern."
> They clearly go far beyond this and generate completely novel ideas that are not in the training data.
There's a case where this is trivially false. Language. LLMs are bound by language that was invented by humans. They are unable to "conceive" of anything that cannot be described by human language as it exists, whereas humans create new words for new ideas all the time.
I just asked ChatGPT to make up a Chinese word for hungry+angry. It came up with a completely novel word that actually sounds okay: 饥怒. It then explained to me how it came up with the word.
You can't claim that that isn't understanding. It just strikes me that we've moved the goalposts into every more esoteric corners: sure, ChatGPT seems like it can have a real conversation, but can it do X extremely difficult task that I just thought up?
Uh, I believe you're really confused on things like ChatGPT versus LLMs in general. You don't have to feed human language to an LLM for them to learn things. You can feed wifi data waveforms for example and they can 'learn' insights from that.
Furthermore you're thinking here doesn't even begin to explain multimodal models at all.
The problem with calling an LLM a parrot is that anyone who has actually interacted with an LLM knows that it produces completely novel responses to questions it has never seen before. These answers are usually logical and reasonable, based on both the information you gave the LLM and its previous knowledge of the world. Doing that requires understanding.
> They never make new connections that aren't in training data.
This is just categorically untrue. They make all sorts of logical connections that are not explicitly contained in the training data. Making logical inferences about subjects one has never heard about - based on the things one does know - is an expression of understanding. LLMs do that.