  Knowledge distillation (KD) requires sufficient data to transfer knowledge
from large-scale teacher models to small-scale student models. Therefore, data
augmentation has been widely used to mitigate the shortage of data under
specific scenarios. Classic data augmentation techniques, such as synonym
replacement and k-nearest-neighbors, are initially designed for fine-tuning. To
avoid severe semantic shifts and preserve task-specific labels, those methods
prefer to change only a small proportion of tokens (e.g., changing 10% tokens
is generally the best option for fine-tuning). However, such data augmentation
methods are sub-optimal for knowledge distillation since the teacher model
could provide label distributions and is more tolerant to semantic shifts. We
first observe that KD prefers as much data as possible, which is different from
fine-tuning that too much data will not gain more performance. Since changing
more tokens leads to more semantic shifts, we use the proportion of changed
tokens to reflect semantic shift degrees. Then we find that KD prefers
augmented data with a larger semantic shift degree (e.g., changing 30% tokens
is generally the best option for KD) than fine-tuning (changing 10% tokens).
Besides, our findings show that smaller datasets prefer larger degrees until
the out-of-distribution problem occurs (e.g., datasets with less than 10k
inputs may prefer the 50% degree, and datasets with more than 100k inputs may
prefer the 10% degree). Our work sheds light on the preference difference in
data augmentation between fine-tuning and knowledge distillation and encourages
the community to explore KD-specific data augmentation methods.
