  Active learning, which effectively collects informative unlabeled data for
annotation, reduces the demand for labeled data. In this work, we propose to
retrieve unlabeled samples with a local sensitivity and hardness-aware
acquisition function. The proposed method generates data copies through local
perturbations and selects data points whose predictive likelihoods diverge the
most from their copies. We further empower our acquisition function by
injecting the select-worst case perturbation. Our method achieves consistent
gains over the commonly used active learning strategies in various
classification tasks. Furthermore, we observe consistent improvements over the
baselines on the study of prompt selection in prompt-based few-shot learning.
These experiments demonstrate that our acquisition guided by local sensitivity
and hardness can be effective and beneficial for many NLP tasks.
