  In the era of large language models (LLMs), in-context learning (ICL) stands
out as an effective prompting strategy that explores LLMs' potency across
various tasks. However, applying LLMs to grammatical error correction (GEC) is
still a challenging task. In this paper, we propose a novel
ungrammatical-syntax-based in-context example selection strategy for GEC.
Specifically, we measure similarity of sentences based on their syntactic
structures with diverse algorithms, and identify optimal ICL examples sharing
the most similar ill-formed syntax to the test input. Additionally, we carry
out a two-stage process to further improve the quality of selection results. On
benchmark English GEC datasets, empirical results show that our proposed
ungrammatical-syntax-based strategies outperform commonly-used word-matching or
semantics-based methods with multiple LLMs. This indicates that for a
syntax-oriented task like GEC, paying more attention to syntactic information
can effectively boost LLMs' performance. Our code will be publicly available
after the publication of this paper.
