Abstract: We investigate the capability of a transformer pretrained on natural language to generalize to other modalities with minimal finetuning -- in particular, without finetuning of the self-attention and feedforward layers of the residual blocks. We consider such a model, which we call a Frozen Pretrained Transformer (FPT), and study finetuning it on a variety of sequence classification tasks spanning numerical computation, vision, and protein fold prediction. In contrast to prior works which investigate finetuning on the same modality as the pretraining dataset, we show that pretraining on natural language improves performance and compute efficiency on non-language downstream tasks. In particular, we find that such pretraining enables FPT to generalize in zero-shot to these modalities, matching the performance of a transformer fully trained on these tasks.
Authors: Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch (UC Berkeley, Facebook AI Research, Google Brain)
0:00 - Intro & Overview
2:00 - Frozen Pretrained Transformers
4:50 - Evaluated Tasks
10:05 - The Importance of Training LayerNorm
17:10 - Modality Transfer
25:10 - Network Architecture Ablation
26:10 - Evaluation of the Attention Mask
27:20 - Are FPTs Overfitting or Underfitting?
28:20 - Model Size Ablation
28:50 - Is Initialization All You Need?
31:40 - Full Model Training Overfits
32:15 - Again the Importance of Training LayerNorm
33:10 - Conclusions & Comments