I think the fact alone that distillation and quantization are techniques that can produce substantial improvements is a strong sign that we still have no real comprehensive understanding how the models work.
If we had, there would be no reason to train a model with more parameters than are strictly necessary to represent the space's semantic structure. But then it should be impossible for distilled models with less parameters to come close to the performance of the original model.
Yet this is what happens - the distilled or quantized models often come very close to the original model.
So I think there are still many low-hanging fruits to pick.
If we had, there would be no reason to train a model with more parameters than are strictly necessary to represent the space's semantic structure. But then it should be impossible for distilled models with less parameters to come close to the performance of the original model.
Yet this is what happens - the distilled or quantized models often come very close to the original model.
So I think there are still many low-hanging fruits to pick.