  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.
