While probing is a common technique for identifying knowledge in the representations of pretrained models, it is unclear whether this technique can explain the downstream success of models like BERT which are trained end-to-end during finetuning. To address this question, we compare probing with a different measure of transferability: the decrease in finetuning performanceof a partially-reinitialized model . This technique reveals that in BERT, layers with high probing accuracy on downstream GLUE tasks are neither necessary nor sufficient for high accuracy on those tasks. In addition, dataset size impacts layer transferability: the less finetuning data one has, the more important the middle and later layers of BERT become. Furthermore, BERT does not simply find a better initializer for individual layers; instead, interactions between layers matter and reordering BERT's layers prior to finetuning significantly harms evaluation metrics. These results provide a way of understanding the transferability of parameters in pretrained language models, revealing the fluidity and complexity of transfer learning in these models .
How does language model pretraining help transfer learning? We consider a simple ablation technique for determining the impact of each pretrained layer on transfer task performance. This method, partial reinitialization, involves replacing different layers of a pretrained model with random weights, then finetuning the entire model on the transfer task and observing the change in performance . This technique reveals that in BERT, layers with high probing performance on downstream GLUE tasks are neither necessary nor sufficient for high accuracy on those tasks. Furthermore, the benefit of using pretrained parameters for a layer varies dramatically with finetuning dataset size: parameters that provide tremendous performance improvement when data is plentiful may provide negligible benefits in data-scarce settings. These results reveal the complexity of the transfer learning process, highlighting the limitations of methods that operate on frozen models or single data samples .
R
While probing is a common technique for identifying knowledge in the representations of pretrained models, it is unclear whether this technique can explain the downstream success of models like BERT which are trained end-to-end during finetuning. To address this question, we compare probing with a different measure of transferability: the decrease in finetuning performanceof a partially-reinitialized model
How does language model pretraining help transfer learning? We consider a simple ablation technique for determining the impact of each pretrained layer on transfer task performance. This method, partial reinitialization, involves replacing different layers of a pretrained model with random weights, then finetuning the entire model on the transfer task and observing the change in performance
R
accuracy
performance
R
In addition, dataset size impacts layer transferability: the less finetuning data one has, the more important the middle and later layers of BERT become. Furthermore, BERT does not simply find a better initializer for individual layers; instead, interactions between layers matter and reordering BERT's layers prior to finetuning significantly harms evaluation metrics. These results provide a way of understanding the transferability of parameters in pretrained language models, revealing the fluidity and complexity of transfer learning in these models
Furthermore, the benefit of using pretrained parameters for a layer varies dramatically with finetuning dataset size: parameters that provide tremendous performance improvement when data is plentiful may provide negligible benefits in data-scarce settings. These results reveal the complexity of the transfer learning process, highlighting the limitations of methods that operate on frozen models or single data samples