Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency

Min Zeng, Jiexin Kuang, Mengyang Qiu, Jayoung Song, Jungyeul Park
2/24/2024
cs.CL

No confident code implementation found for this paper.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{zeng2024evaluating,
  title  = {Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency},
  author = {Min Zeng and Jiexin Kuang and Mengyang Qiu and Jayoung Song and Jungyeul Park},
  year   = {2024},
  eprint = {2402.15930},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.15930v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2402.15930.svg)](https://codeofpaper.com/paper/2402.15930)

Discussion