  Pre-trained language models have recently emerged as a powerful tool for
fine-tuning a variety of language tasks. Ideally, when models are pre-trained
on large amount of data, they are expected to gain implicit knowledge. In this
paper, we investigate the ability of pre-trained language models to generalize
to different non-language tasks. In particular, we test them on tasks from
different domains such as computer vision, reasoning on hierarchical data, and
protein fold prediction. The four pre-trained models that we used, T5, BART,
BERT, and GPT-2 achieve outstanding results. They all have similar performance
and they outperform transformers that are trained from scratch by a large
margin. For instance, pre-trained language models perform better on the Listops
dataset, with an average accuracy of 58.7\%, compared to transformers trained
from scratch, which have an average accuracy of 29.0\%. The significant
improvement demonstrated across three types of datasets suggests that
pre-training on language helps the models to acquire general knowledge,
bringing us a step closer to general AI. We also showed that reducing the
number of parameters in pre-trained language models does not have a great
impact as the performance drops slightly when using T5-Small instead of
T5-Base. In fact, when using only 2\% of the parameters, we achieved a great
improvement compared to training from scratch. Finally, in contrast to prior
work, we find out that using pre-trained embeddings for the input layer is
necessary to achieve the desired results.
