@inproceedings{zeng-etal-2024-evaluating,
    title = "Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency",
    author = "Zeng, Min  and
      Kuang, Jiexin  and
      Qiu, Mengyang  and
      Song, Jayoung  and
      Park, Jungyeul",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.569",
    pages = "6426--6430",
    abstract = "This paper proposes an analysis of prompting strategies for grammatical error correction (GEC) with selected large language models (LLM) based on language proficiency. GEC using generative LLMs has been known for overcorrection where results obtain higher recall measures than precision 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.",
}

This paper proposes an analysis of prompting strategies for grammatical error correction (GEC) with selected large language models (LLM) based on language proficiency. GEC using generative LLMs has been known for overcorrection where results obtain higher recall measures than precision 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.
Zeng, Min Kuang, Jiexin Qiu, Mengyang Song, Jayoung Park, Jungyeul
Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency