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。

评估基于语言能力的语法错误修正提示策略

Evaluating Prompting Strategies for Grammatical Error Correction Based  on Language Proficiency

Chinese grammatical error correction (CGEC) faces serious overcorrection
challenges when employing autoregressive generative models such as
sequence-to-sequence (Seq2Seq) models and decoder-only large language models
(LLMs). While previous methods aim to address overcorrection in Seq2Seq models,
they are difficult to adapt to decoder-only LLMs. In this paper, we propose an
alignment-enhanced corrector for the overcorrection problem that applies to
both Seq2Seq models and decoder-only LLMs. Our method first trains a correction
model to generate an initial correction of the source sentence. Then, we
combine the source sentence with the initial correction and feed it through an
alignment model for another round of correction, aiming to enforce the
alignment model to focus on potential overcorrection. Moreover, to enhance the
model's ability to identify nuances, we further explore the reverse alignment
of the source sentence and the initial correction. Finally, we transfer the
alignment knowledge from two alignment models to the correction model,
instructing it on how to avoid overcorrection. Experimental results on three
CGEC datasets demonstrate the effectiveness of our approach in alleviating
overcorrection and improving overall performance.

本文提出了一种用于解决过度更正问题的对齐增强纠错模型，适用于序列到序列模型和仅具有解码器的大型语言模型。该方法通过训练纠错模型生成源句子的初始纠错，并将其与源句子结合，经过对齐模型进行另一轮纠错，以确保对齐模型专注于潜在的过度更正。此外，为了增强模型识别微妙差别的能力，我们还探索了源句子和初始纠错的反向对齐。最后，我们将两个对齐模型的对齐知识传递给纠错模型，指导其避免过度更正。在三个中文语法错误纠正数据集上的实验结果表明，我们的方法在减轻过度更正和提高整体性能方面是有效的。