  The emergence of in-context learning (ICL) enables large pre-trained language
models (PLMs) to make predictions for unseen inputs without updating
parameters. Despite its potential, ICL's effectiveness heavily relies on the
quality, quantity, and permutation of demonstrations, commonly leading to
suboptimal and unstable performance. In this paper, we tackle this challenge
for the first time from the perspective of demonstration augmentation.
Specifically, we start with enriching representations of demonstrations by
leveraging their deep feature distribution. We then theoretically reveal that
when the number of augmented copies approaches infinity, the augmentation is
approximately equal to a novel logit calibration mechanism integrated with
specific statistical properties. This insight results in a simple yet highly
efficient method that significantly improves the average and worst-case
accuracy across diverse PLMs and tasks. Moreover, our method effectively
reduces performance variance among varying demonstrations, permutations, and
templates, and displays the capability to address imbalanced class
distributions.
