Motore said:
Hmm I prompted ChatGPT with the word "poem" several times and every time it generted a random poem.
I overstated when I said that the poems are "about poetry", but in a test where I started eight sessions and said "poem", five of the poems self referenced the act of writing a poem in some way. That is very unusual for poems to do, so we know they are not just cobbled together from poetry in some random kind of way. (The poems also generally are about nature, and the term "canvas" appears somewhere in almost all 8, surprisingly, so for some strange reason the training has zeroed in on a few somewhat specific themes when poetry is involved.) But the larger issue is that ChatGPT gives some kind of special significance to the prompt, it is trained in some way to treat the prompt as special and it was a bit of a slog to figure out from Wolfram's description just how that special status is enforced in the training process, apparently a crucial element is that it is trained to "model" language in a way that involves responses to prompts. ChatGPT also wouldn't explain it when I asked it. All I can say is that it appears to be a very specific type of language that it is modeling, in effect a way of predicting the next word that in some way reacts to a prompt, rather than just predicting the next word in a random body of text. (You could imagine training an LLM to do the latter, but you would not get ChatGPT that way, both Wolfram and ChatGPT itself refer to other aspects of the training process and the way the language model works but the specifics are far from clear to me.)
Motore said:
It is just scrabling text so that the natural language is uphold and that it rhymes (so that it actually looks like a poem, which there has to be presumably millions of them in the training data). Why would the trainers need to add anything?
They need to add the concept of a prompt, and how to alter the training in response to that.
Motore said:
Well sure, that is how LLMs are constructed. You could construct one without prompt and it will just write something random in a natural language somwhere at random time. Not really useful.
Yes exactly. So we should not say the LLMs are just predicting words that come next, they are doing it in a rather specific way that gives special status to the prompt. They also appear to give special status to a prompt that they are trained to interpret as a correction. This seems to be a difference between ChatGPT and Bard, for example, because in my experience ChatGPT is trained to respond to correction in a much more obsequious way than Bard is. (For example, if you try to correct both into saying that one plus one is three, ChatGPT will say you must be using a different mathematical system, or perhaps are even making a joke (!), while Bard is far less forgiving and said "this is a nonsensical question because 1+1 cannot equal 3. If 1+1=3, then the entire concept of mathematics breaks down", which is certainly not true because I can easily imagine a mathematical system which always upticks any answer of a binary integer arithmetical operation, and mathematics in that system does not break down. Thus Bard not only fails to be obsequious, it fails to be correct in its nonobseqiousness!)
Motore said:
Well sure, it has to be trained on, but who said otherwise? Trained on massive data not by people. They just review the responses and give feedback so ChatGPT can optimize itself (as you can also do). At the end of the day It's just predicting which word comes next.
The people do the training because they decide how the training will work. So that's not just predicting what word comes next, although it is mostly that. But it is predicting what word comes next in a very carefully orchestrated environment, and Wolfram makes it clear that the people don't completely understand why certain such environments work better than others, but he describes it as an "art", and there's nothing automatic in performing an artform.