Comment by zmmmmm
2 years ago
> because for certain prompts, the approximate consensus of all available text on the internet is pretty much true
I think you're slightly mischaracterising things here. It has potential to be at least slightly and possibly much better than that. This is evidenced by the fact it is much better than chance at answering "novel" questions that don't have a direct source in the training data. Why it can do it is because at a certain point, to solve the optimisation problem of "what word comes next" the least complex strategy actually becomes to start modeling principles of logic and facts connecting them. It is not in any systematic or reliable way so you can't ever guarantee when or how well it is going to apply these, but it is absolutely learning higher order patterns than simple text / pattern matching, and it is absolutely able to generalise these across topics.
You’re absolutely right and I’m sure that something resembling higher-level pattern matching is present in the architecture and weights of the model, I’m just saying that I’m not aware of “logical thought” being explicitly optimized or designed for - it’s more of a sometimes-emergent feature of a machine that tries to approximate the content of the internet, which for some topics is dominated by mostly logical thought. I’m also unaware of a ground truth against which “correct facts” could even be trained for..
> I’m also unaware of a ground truth against which “correct facts” could even be trained for..
Seems like there are quite a few obvious possibilities here off the top of my head. Ground truth for correct facts could be:
1) Wikidata
2) Mathematical ground truth (can be both generated and results validated automatically) including physics
3) Programming ground truth (can be validated by running the code and defining inputs/outputs)
4) Chess
5) Human labelled images and video
6) Map data
7) Dependent on your viewpoint, peer reviewed journals, as long as cited with sources.