  In-context learning (ICL) suffers from oversensitivity to the prompt, making
it unreliable in real-world scenarios. We study the sensitivity of ICL with
respect to multiple perturbation types. First, we find that label bias obscures
the true sensitivity, and therefore prior work may have significantly
underestimated ICL sensitivity. Second, we observe a strong negative
correlation between ICL sensitivity and accuracy: predictions sensitive to
perturbations are less likely to be correct. Motivated by these findings, we
propose \textsc{SenSel}, a few-shot selective prediction method that abstains
from sensitive predictions. Experiments on ten classification datasets show
that \textsc{SenSel} consistently outperforms two commonly used
confidence-based and entropy-based baselines on abstention decisions.
