I think the more serious and interesting problem is how to prompt GPT to produce more serious text. If we just give it all existing text to learn from, it will have to model both the stupid and the smart, which is fine, since a smart person should be able to impersonate a stupid one without losing their capabilities. The problem we seem to have is that we can't efficiently tell GPT when to pretend to be stupid and when not to do so, right now it seems to be picking up on cues we give it from the prompt. If our prompt looks like something someone on reddit wrote, the response is also stupid, if the prompt looks like a paragraph from a scientific paper, the response is likely to be GPT's best attempt at reasoning.
So in my view one of the best ways to improve GPT is to tag every piece of text it learns from with its provenance and date of publication. These would be "tags from god" so to speak, so even if a piece of text it trains on is a story that starts with "it was the year 2034...", the tag for that piece of text would specify "publication date: 1985", so GPT would not be fooled into confusing stories written about the future, with the actual future. It would also not confuse scientific articles with just random blog posts, or again stories. So when we prompt it at inference-time and give it "Introduction to Fusion Plant Design, Cambridge University press, Chapter 1: ", we can tag our prompt with both "scientific article" and "publication date: 2040" in order to ensure that GPT is trying to produce a legitimate article and actually trying to model the future progress of science. Giving it this prompt right now would likely produce rubbish, because it might infer that the title to that textbook appears in a random comment like this one, and so GPT wouldn't actually try to produce a textbook.
Tagging all text with the date it was produced is very important for other reasons as well. (And as far as I know, it isn't done at present.)
With such date tags, one could ask GPT to produce text for a specific date. For example, one could ask it to continue the prompt "I think people of different races should" with dates of 1880, 1920, 1960, and 2000 to see how racial attitudes have changed over the years, which is very interesting historical information. It also would allow one to force a 2023 date for normal responses, to avoid unwante...
I think that prompting is definitely important. I've found that GPT as it is now can mimic any given author's style with great accuracy as long as it's given that author's text inside of the prompt. For example, "write a short story in the style of Nabokov" gives you a bland short story, while prompting with his verbatim text produces a pretty faithful continuation.
Tagging is conditioning (see also). If instead of having SSL model learn text
, you have it learn (summary, text)
, then it learns to predict text
from summary
. A summary can be any measurement of text such that some values of that measurement can be a useful query. For example, if text
is a chess game, a summary
could be a statement of who wins. Then, starting a prompt with a claim of winning will tend to lead to a winning game. Similarly, if summary
says if text
is a valid argument, you gain the ability to query for valid arguments. Finally, summary
can we...
See the Galatica model (https://arxiv.org/abs/2211.09085) from Meta. It's trained on a curated dataset of scientific papers, reference materials and scientific knowledge bases (with only a very small % of random internet text). IIRC the benefits of this seem limited (better to train on a bigger dataset and use other techniques to make the model access the sciencey parts of the training set).
(Epistemic status: I do not understand GPT deeply, so this is just a random idea.)
If I understand it correctly, GPT learns from existing texts. Lots of texts.
Would it be possible to make GPT smarter by simply giving it smarter text? Of course, writing tons of smarter text would be a lot of work, but what about annotating the existing text, like "take this more seriously" and "take this less seriously"? (From technical perspective, maybe the GPT should read the text marked as serious five times?) Assuming that the annonation is roughly correct, would this improve the results?
*
If yes, the problem is how to select smarter texts, especially if we want lots of them? But I think some good guesses can be made: