If a model trained on synthetic data is expected to have good performance out of distribution (on real-world problems) then I think that it would also be expected to have high performance at assessing whether it’s in a simulation. Narrowing its domain of expertise sounds distinctly harder than using mostly synthetic data.
If it’s a model limited to e.g. the world of true mathematical theorems from synthetic data then perhaps this would narrow its capabilities enough. I don’t know what happens if such a model starts investigating theorems about decision theory and statistical inference and machine learning. At some point, self-identification seems likely. I am not sure how to test the effectiveness of synthetic data on models that achieve self-awareness.
If a model trained on synthetic data is expected to have good performance out of distribution (on real-world problems) then I think that it would also be expected to have high performance at assessing whether it’s in a simulation. Narrowing its domain of expertise sounds distinctly harder than using mostly synthetic data.
If it’s a model limited to e.g. the world of true mathematical theorems from synthetic data then perhaps this would narrow its capabilities enough. I don’t know what happens if such a model starts investigating theorems about decision theory and statistical inference and machine learning. At some point, self-identification seems likely. I am not sure how to test the effectiveness of synthetic data on models that achieve self-awareness.