  Transformers achieve state-of-the-art accuracy and robustness across many
tasks, but an understanding of the inductive biases that they have and how
those biases are different from other neural network architectures remains
elusive. Various neural network architectures such as fully connected networks
have been found to have a simplicity bias towards simple functions of the data;
one version of this simplicity bias is a spectral bias to learn simple
functions in the Fourier space. In this work, we identify the notion of
sensitivity of the model to random changes in the input as a notion of
simplicity bias which provides a unified metric to explain the simplicity and
spectral bias of transformers across different data modalities. We show that
transformers have lower sensitivity than alternative architectures, such as
LSTMs, MLPs and CNNs, across both vision and language tasks. We also show that
low-sensitivity bias correlates with improved robustness; furthermore, it can
also be used as an efficient intervention to further improve the robustness of
transformers.
