The writing examples of English language learners may be different from those
of native speakers. Given that there is a significant differences in second
language (L2) learners' error types by their proficiency levels, this paper
attempts to reduce overcorrection by examining the interaction between LLM's
performance and L2 language proficiency. Our method focuses on zero-shot and
few-shot prompting and fine-tuning models for GEC for learners of English as a
foreign language based on the different proficiency. We investigate GEC results
and find that overcorrection happens primarily in advanced language learners'
writing (proficiency C) rather than proficiency A (a beginner level) and
proficiency B (an intermediate level). Fine-tuned LLMs, and even few-shot
prompting with writing examples of English learners, actually tend to exhibit
decreased recall measures. To make our claim concrete, we conduct a
comprehensive examination of GEC outcomes and their evaluation results based on
language proficiency.

通过研究英语作为第二语言的学习者的表现和语言熟练度之间的相互作用，我们的方法专注于基于不同熟练程度的零样本和少样本提示和微调模型，以提高外语英语学习者的语法错误修正（GEC）效果。我们发现，过度修正主要发生在高级语言学习者（熟练程度 C）的写作中，而不是熟练程度 A（初学者水平）和熟练程度 B（中级水平）。经过微调的 LLMs，甚至少量提示与英语学习者的写作范例，实际上会导致召回率下降。为了证明我们的论断，我们对 GEC 结果及其根据语言熟练度的评估结果进行了全面检查。