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.

本文探讨了使用零样本和少量样本设置，以 GPT-3 为基础进行语法错误修正任务的提示式方法的性能和可控性，并发现给定适当的任务说明和示例时，GPT-3 能够实现控制性能，显着增强学习过程。