mlsanityData to 7 Oct 2026

Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency

arXiv 2402.15930 ↗First posted 24 Feb 2024cs.CLcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 6 of its 12 references that are in the map. First posted on arXiv 24 Feb 2024.

Builds on

6 of 12 references.

References · 6

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2020GECToR -- Grammatical Error Correction: Tag, Not Rewritecited by 177
  3. 2021A Simple Recipe for Multilingual Grammatical Error Correctioncited by 108
  4. 2023Is ChatGPT a Highly Fluent Grammatical Error Correction System? A Comprehensive Evaluationcited by 97
  5. 2022Prompt-and-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer with Small Language Modelscited by 49
  6. 2023Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methodscited by 37

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 5.

9 papers.