Frozen Pretrained Transformers as Universal Computation Engines


  • Kevin Lu UC Berkeley Facebook AI Research
  • Aditya Grover UCLA Facebook AI Research
  • Pieter Abbeel UC Berkeley
  • Igor Mordatch Google Brain



Machine Learning (ML), Speech & Natural Language Processing (SNLP), Computer Vision (CV), Knowledge Representation And Reasoning (KRR)


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 can improve performance and compute efficiency on non-language downstream tasks. Additionally, we perform an analysis of the architecture, comparing the performance of a random initialized transformer to a random LSTM. Combining the two insights, we find language-pretrained transformers can obtain strong performance on a variety of non-language tasks.




How to Cite

Lu, K., Grover, A., Abbeel, P., & Mordatch, I. (2022). Frozen Pretrained Transformers as Universal Computation Engines. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7628-7636.



AAAI Technical Track on Machine Learning II