But there is no world representation inside an LLM, only text (words, letters) representations, so nothing the LLM does can be based on reasoning in a traditional sense.
I would wager that if we build an LLM based on a training data set collection, and then we rebuild it with a heavily edited version of the data set that explicitly excludes certain significant areas of human discourse, the LLM will be severely impaired in its apparent ability to "reason" about anything connected with the excluded areas. That sounds as if it ought to surprise you, since you think they are capable of reasoning beyond the training set. It wouldn't surprise me at all, since I do not believe that it what they are doing.
LLMs contain a model of human speech (really text) behavior that is almost unimaginably more complex than anything we've built before. But by itself that doesn't mean very much with respect to general reasoning ability. The fact that LLMs can convince you otherwise points, to me, to the richness of the training data in suitable responses to almost any prompt,suitable, that is, for the purpose of persuading you that there is some kind of reasoning occuring. But there is not. The fact that neither you nor I can really build a model (hah!) of what the LLM is actually doing doesn't change that.
Are you saying that NLP as a field of research did not exist before LLMs? This is a continuation of research that has been in progress for decades.
> But there is no world representation inside an LLM, only text (words, letters) representations, so nothing the LLM does can be based on reasoning in a traditional sense.
Not true. The model has learned a representation of semantic relationships between words and concepts at multiple levels of abstraction. That is the entire point. That's what is was trained to do.
It's a vast and deep neural network with a very high dimensional representation of the data. Those semantic/meaning relations are automatically learned and encoded in the model.
> It's a vast and deep neural network with a very high dimensional representation of the data.
the data is text, so ...
It's a vast and deep neural network with a very high dimensional representation of *text*
And yes, to some extent, text represents the world in interesting ways. But not adequately, IMO.
If you were an alien seeking to understand the earth, starting with humans' textual encoding thereof might be a palce to start. But its inadequacies would rapidly become evident, I claim, and you would realize that you need a "vast and deep representation" of the actual planet.
> Are you saying that NLP as a field of research did not exist before LLMs? This is a continuation of research that has been in progress for decades.
Of course I'm not saying that (the first sentence). Part of my whole point is that LLMs are to NLPs as rockets are to airplanes. They're fundamentally a "rip it up and start again" approach, that discards almost everything everyone knew about NLP. The results are astounding, but the connection with, yes, "traditional" NLP is tenuous.
So what I'm saying is, GPT "knows" what a cat is. It "knows" what an orange is. It has inferred these concepts from the data set.
Imagine approaching someone who is tripping on LSD and demanding they immediately solve a 10 digit multiplication problem, then saying "AHA! You cannot solve it, therefore you are incapable of any reasoning whatsoever!"
> reasoning in a traditional sense.
We are talking about reasoning in a general sense. There are many types of reasoning in AI which I'm sure you know how to look up and read about. "Traditional" is not one of the categories.
