  Large language models (LLMs) have shown impressive performance on downstream
tasks by in-context learning (ICL), which heavily relies on the quality of
demonstrations selected from a large set of annotated examples. Recent works
claim that in-context learning is robust to noisy demonstrations in text
classification. In this work, we show that, on text generation tasks, noisy
annotations significantly hurt the performance of in-context learning. To
circumvent the issue, we propose a simple and effective approach called Local
Perplexity Ranking (LPR), which replaces the "noisy" candidates with their
nearest neighbors that are more likely to be clean. Our method is motivated by
analyzing the perplexity deviation caused by noisy labels and decomposing
perplexity into inherent perplexity and matching perplexity. Our key idea
behind LPR is thus to decouple the matching perplexity by performing the
ranking among the neighbors in semantic space. Our approach can prevent the
selected demonstrations from including mismatched input-label pairs while
preserving the effectiveness of the original selection methods. Extensive
experiments demonstrate the effectiveness of LPR, improving the EM score by up
to 18.75 on common benchmarks with noisy annotations.
