Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction

arXiv:2403.19283v1 Announce Type: new
Abstract: 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 …

abstract arxiv context cs.cl error error correction example gec however in-context learning language language models large language large language models llms novel paper prompting strategy syntax tasks type