Research

Self-supervised pre-training of large-scale transformer models on text corpora followed by fine-tuning has achieved state-of-the-art on a number of natural language processing tasks. Recently, Lu et al. (2021) claimed that frozen pretrained transformers (FPTs) match or outperform training from scratch as well as unfrozen (fine-tuned) pretrained transformers in a set of transfer tasks to other modalities. In our work, we find that this result is, in fact, an artefact of not tuning the learning rates. After carefully redesigning the empirical setup, we find that when tuning learning rates properly, pretrained transformers do outperform or match training from scratch in all of our tasks, but only as long as the entire model is fine-tuned. Thus, while transfer from pre-trained language models to other modalities does indeed provide gains and hints at exciting possibilities for future work, properly tuning hyperparameters is important for arriving at robust findings.