ChatGPT, a large-scale language model based on the advanced GPT-3.5 architecture, has shown remarkable potential in various Natural Language Processing (NLP) tasks. However, there is currently a dearth of comprehensive study exploring its potential in the area of Grammatical Error Correction (GEC). To showcase its capabilities in GEC, we design zero-shot chain-of-thought (CoT) and few-shot CoT settings using in-context learning for ChatGPT. Our evaluation involves assessing ChatGPT's performance on five official test sets in three different languages, along with three document-level GEC test sets in English. Our experimental results and human evaluations demonstrate that ChatGPT has excellent error detection capabilities and can freely correct errors to make the corrected sentences very fluent, possibly due to its over-correction tendencies and not adhering to the principle of minimal edits. Additionally, its performance in non-English and low-resource settings highlights its potential in multilingual GEC tasks. However, further analysis of various types of errors at the document-level has shown that ChatGPT cannot effectively correct agreement, coreference, tense errors across sentences, and cross-sentence boundary errors.

翻译：ChatGPT作为基于先进GPT-3.5架构的大规模语言模型，在各类自然语言处理（NLP）任务中已展现出显著潜力。然而，目前尚缺乏系统研究探讨其在语法错误修正（GEC）领域的应用潜力。为展示ChatGPT在GEC中的能力，我们利用上下文学习设计了零样本思维链（CoT）与少样本CoT设置。评估涉及ChatGPT在三种语言的五个官方测试集以及三个英语文档级GEC测试集上的表现。实验与人工评估表明，ChatGPT具备卓越的错误检测能力，并能自由修正错误使句子高度流畅，这可能归因于其过度修正倾向且未遵循最小编辑原则。此外，其在非英语及低资源场景中的表现凸显了在多语言GEC任务中的潜力。但文档级错误类型深入分析显示，ChatGPT无法有效修正跨句一致性、共指、时态错误及跨句边界错误。