Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency
This addresses the issue of overcorrection in language learning tools for English learners, but it is incremental as it builds on existing prompting and fine-tuning methods.
The paper tackled the problem of overcorrection in grammatical error correction for English language learners by analyzing how large language models perform across different proficiency levels, finding that overcorrection primarily occurs in advanced learners' writing and that fine-tuning or few-shot prompting can reduce recall measures.
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.