Speculations on (near) Out-Of-Distribution (OOD) regimes - [Absence of extractable information] The model can no longer extract any relevant information. Models may behave more and more similarly to their baseline behavior in this regime. Models may learn the heuristic to ignore uninformative data, and this heuristic may generalize pretty far. Publication supporting this regime: Deep Neural Networks Tend To Extrapolate Predictably - [Extreme information] The model can still extract information, but the features extracted are becoming extreme in value (“extreme” = range never seen during training). Models may keep behaving in the same way as “at the In-Distrubution (ID) border”. Models may learn the heuristic that for extreme inputs, you should keep behaving as if you were still in the embedding same direction but still ID. - [Inner OOD] The model observes a mix of features-values that it never saw during training, but none of these features-values are by themselves OOD. For example, the input is located between two populated planes. Models may learn the heuristic to use a (mixed) policy composed of closest ID behaviors. - [Far/Disrupting OOD]: This happens in one of the other three regimes when the inputs break the OOD heuristics learned by the model. These can be found by adversarial search or by moving extremely OOD. - [Fine-Tuning (FT) or Jailbreaking OOD] The inference distribution is OOD of the distribution during the FT. The model then stops using heuristics defined during the FT and starts using those learned during pretraining (the inference is still ID with respect to the pretraining distribution).
Speculations on (near) Out-Of-Distribution (OOD) regimes
- [Absence of extractable information] The model can no longer extract any relevant information. Models may behave more and more similarly to their baseline behavior in this regime. Models may learn the heuristic to ignore uninformative data, and this heuristic may generalize pretty far. Publication supporting this regime: Deep Neural Networks Tend To Extrapolate Predictably
- [Extreme information] The model can still extract information, but the features extracted are becoming extreme in value (“extreme” = range never seen during training). Models may keep behaving in the same way as “at the In-Distrubution (ID) border”. Models may learn the heuristic that for extreme inputs, you should keep behaving as if you were still in the embedding same direction but still ID.
- [Inner OOD] The model observes a mix of features-values that it never saw during training, but none of these features-values are by themselves OOD. For example, the input is located between two populated planes. Models may learn the heuristic to use a (mixed) policy composed of closest ID behaviors.
- [Far/Disrupting OOD]: This happens in one of the other three regimes when the inputs break the OOD heuristics learned by the model. These can be found by adversarial search or by moving extremely OOD.
- [Fine-Tuning (FT) or Jailbreaking OOD] The inference distribution is OOD of the distribution during the FT. The model then stops using heuristics defined during the FT and starts using those learned during pretraining (the inference is still ID with respect to the pretraining distribution).