  Previous research has demonstrated that natural language explanations provide
valuable inductive biases that guide models, thereby improving the
generalization ability and data efficiency. In this paper, we undertake a
systematic examination of the effectiveness of these explanations. Remarkably,
we find that corrupted explanations with diminished inductive biases can
achieve competitive or superior performance compared to the original
explanations. Our findings furnish novel insights into the characteristics of
natural language explanations in the following ways: (1) the impact of
explanations varies across different training styles and datasets, with
previously believed improvements primarily observed in frozen language models.
(2) While previous research has attributed the effect of explanations solely to
their inductive biases, our study shows that the effect persists even when the
explanations are completely corrupted. We propose that the main effect is due
to the provision of additional context space. (3) Utilizing the proposed
automatic perturbed context, we were able to attain comparable results to
annotated explanations, but with a significant increase in computational
efficiency, 20-30 times faster.
