This isn’t quite the threat model I normally think about when discussing these kinds of problems. I imagine that we have two variables: looks-good-low-effort and is-good, which are correlated at some pretty high level on a non-pathological dataset, say 0.8. The dataset is then labelled according to looks-good-low-effort. The AI learns to put ~all its weight on learns-good-low-effort because that’s the best possible predictor.
What you want is some set of data which ties on is-good but varies on looks-good-low-effort. Unfortunately, you don’t have access to is-good, but you can access looks-good-high-effort which is correlated at a higher level, say 0.95, with is-good, with the resulting error very strongly correlated with that of looks-good-low-effort. So you produce some items which tie on looks-good-high-effort, and so presumably approximately tie on is-good, but have more noise on looks-good-low-effort.
Cross-domain split also seems like a problem.
I think this maybe makes sense, but I’d like a real test of it before drawing conclusions.
Yep, that all sounds right to me! If you can only access looks-good-high-effort, tie training can’t take you beyond that, but it will shrink the weight on looks-good-low-effort (along with any other spurious features that happen to differ across your pairs).
This isn’t quite the threat model I normally think about when discussing these kinds of problems. I imagine that we have two variables: looks-good-low-effort and is-good, which are correlated at some pretty high level on a non-pathological dataset, say 0.8. The dataset is then labelled according to looks-good-low-effort. The AI learns to put ~all its weight on learns-good-low-effort because that’s the best possible predictor.
What you want is some set of data which ties on is-good but varies on looks-good-low-effort. Unfortunately, you don’t have access to is-good, but you can access looks-good-high-effort which is correlated at a higher level, say 0.95, with is-good, with the resulting error very strongly correlated with that of looks-good-low-effort. So you produce some items which tie on looks-good-high-effort, and so presumably approximately tie on is-good, but have more noise on looks-good-low-effort.
Cross-domain split also seems like a problem.
I think this maybe makes sense, but I’d like a real test of it before drawing conclusions.
Yep, that all sounds right to me! If you can only access looks-good-high-effort, tie training can’t take you beyond that, but it will shrink the weight on looks-good-low-effort (along with any other spurious features that happen to differ across your pairs).