Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction
<|reference_start|>Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction: 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.<|reference_end|>
@article{tang2024ungrammatical-syntax-based,
    title={Ungrammatical-syntax-based In-context Example Selection for Grammatical
  Error Correction},
    author={Chenming Tang, Fanyi Qu and Yunfang Wu},
    journal={arXiv preprint arXiv:2403.19283},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19283},
    primaryClass={cs.CL}
}