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Today's Large Language Models are Essentially BS Machines
(quandyfactory.com)
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this post was submitted on 12 Sep 2023
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You apply a reductionist view to LLMs that you do not apply to humans.
LLMs receive words and produce the next word. Humans receive stimulus from their senses and produce muscle movements.
LLMs are in their infancy, but I'm not convinced their "core loop", so to speak, is any more basic than our own.
In the world of text: text in -> word out
In the physical word: sense stimulation in -> muscle movement out
There's nothing more to it than that, right?
Well, actually there is more to it than that, we have to look at these things on a higher level. If we believe that humans are more than sense stimulation and muscle movements, then we should also be willing to believe that LLMs are more than just a loop producing one word at a time. We need to assess both at the same level of abstraction.
They have no core loop. You are anthropomorphizing them. They are literally no more self-directed than a calculator, and have no more of a "core loop" than a calculator does.
Do you believe humans are simply very advanced and very complicated calculators? I think most people would say "no." While humans can do mathematics, we are different entirely to calculators. We experience sentience; thoughts, feelings, emotions, rationality. None of the devices we've ever built, no matter how clever, has any of those things: and neither do LLMs.
If you do think humans are as deterministic as a calculator then I guess I don't know what to tell you other than I disagree. Other people actually exist and have internal realities. LLMs don't. That's the difference.
As a programmer I can confirm that LLMs definitely have loops. Look at the code, look at the algorithms, you will see the loops. The "core loop" in the LLM algorithm is "read the context, produce the next work, read the context, produce the next word".
The core loop in animals is "receive stimulus using senses, move muscles, receive stimulus using senses, move muscles". That's all humans do, that's all animals do.
I think there's a possibility that humans are simply very advance machines. Look at the debate over whether humans have free will, it's an interesting question and the important take away is that we still have a lot to learn about our brains and physics. I don't want to get into that though.
You've ignored my main complaint. I said that you treat LLMs and humans at different levels of abstraction:
It's not fair to say that LLMs simply predict the next word and humans have feelings and reason.
It would be fair though, to say that LLMs simply predict the next word and humans simply bounce electric-chemical signals between neurons and move muscles.
I don't think that way about people or LLMs though. I think people have feeling and reason, and I think LLMs reason too. LLMs aren't the same as people and aren't as good though. But LLMs are good enough to say that they can "reason" in my experience[0].
[0]: I formed this opinion when learning linear algebra from GPT4. It was quite a good teacher. The textbook I'm using made a mistake that GPT4 caught. I encountered a proof that GPT4 wasn't aware of, and GPT4 wouldn't agree with me that C(A) = C(AA^T) until I explained the proof, and then GPT4 could finally reason for itself and see for itself that C(A) = C(AA^T). As an experiment, I started a new GPT4 session and repeated the experiment using a faulty proof, but I wasn't able to convince GPT4 with a faulty proof, it was able to reason through the math concepts well enough to recognize when a mathematical proof was faulty and could not be convinced by a faulty proof. I tried this experiment 4 or 5 times. To be clear, what happened here is that GPT4 was able to learn a near math concept in one shot (within a single context window), but only if accompanied by a proper mathematical proof, and was smart enough to recognize faulty proofs as being faulty. To me, that rises to the level of "reason".
The two types of loops you equivocate are totally different; saying that a computer executing a program, and an animal living, are actually the same, is very silly indeed. Like, air currents have a "core loop" of blowing around a lot but no one says that they're intelligent or that they're like computer programs or humans.
No; you are analogizing them but losing sense of their differences in the process. I am not abstracting LLMs. That is all they do. That is what they were designed to do and what they accomplish.
You are drawing a comparison between a process humans have that generates consciousness, and literally the entirety of an LLM's existence. There is nothing else to an LLM. Whereas if you say "well a human is basically just bouncing electro-chemical signals between neurons and moving muscles" people (like me) would rightly say you were missing the forest for the trees.
The "trees" for an LLM are their neural networks and word vectors. The forest is a word prediction algorithm. There is no higher level to what they do.
At what level do LLMs teach? Something was teaching me linear algebra and I thought it was the GPT4. When GPT4 was able to recognize a valid mathematical proof that was previously unknown to it, what level was it operating at?
LLMs do not "teach," and that is why learning from them is dangerous. They synthesize words and return other words, but they do not understand the content presented to them in any sense. Because of this, there is the chance that they are simply spouting bullshit.
Learn from them if you like, but remember they are absolutely no substitute for a human, and basically everything they tell you must be checked for correctness.
GPT4 did teach me. I say this as the one who learned, whatever that's worth.