  We explore and improve the capabilities of LLMs to generate data for
grammatical error correction (GEC). When merely producing parallel sentences,
their patterns are too simplistic to be valuable as a corpus. To address this
issue, we propose an automated framework that includes a Subject Selector,
Grammar Selector, Prompt Manager, and Evaluator. Additionally, we introduce a
new dataset for GEC tasks, named ChatLang-8, which encompasses eight types of
subject nouns and 23 types of grammar. It consists of 1 million pairs featuring
human-like grammatical errors. Our experiments reveal that ChatLang-8 exhibits
a more uniform pattern composition compared to existing GEC datasets.
Furthermore, we observe improved model performance when using ChatLang-8
instead of existing GEC datasets. The experimental results suggest that our
framework and ChatLang-8 are valuable resources for enhancing ChatGPT's data
generation capabilities.
