Large-scale pre-trained language models such as GPT-3 have shown remarkable performance across various natural language processing tasks. However, applying prompt-based methods with GPT-3 for Grammatical Error Correction (GEC) tasks and their controllability remains underexplored. Controllability in GEC is crucial for real-world applications, particularly in educational settings, where the ability to tailor feedback according to learner levels and specific error types can significantly enhance the learning process. This paper investigates the performance and controllability of prompt-based methods with GPT-3 for GEC tasks using zero-shot and few-shot setting. We explore the impact of task instructions and examples on GPT-3's output, focusing on controlling aspects such as minimal edits, fluency edits, and learner levels. Our findings demonstrate that GPT-3 could effectively perform GEC tasks, outperforming existing supervised and unsupervised approaches. We also showed that GPT-3 could achieve controllability when appropriate task instructions and examples are given.

翻译：大规模预训练语言模型（如GPT-3）已在各类自然语言处理任务中展现出卓越性能。然而，将基于提示的方法与GPT-3应用于语法错误纠正任务及其可控性研究仍相对不足。在语法错误纠正中，可控性对于实际应用至关重要，尤其在教育场景中——根据学习者水平与具体错误类型定制反馈的能力，能够显著提升学习效果。本文探究了在零样本与小样本设定下，基于提示方法结合GPT-3执行语法错误纠正任务的性能与可控性。我们研究了任务指令与示例对GPT-3输出的影响，重点关注最小编辑、流畅性编辑及学习者水平等控制维度。实验结果表明，GPT-3能有效执行语法错误纠正任务，性能超越现有监督与无监督方法。同时，我们证实当提供恰当的任务指令与示例时，GPT-3能够实现可控性。