  Recently, the development and progress of Large Language Models (LLMs) have
amazed the entire Artificial Intelligence community. Benefiting from their
emergent abilities, LLMs have attracted more and more researchers to study
their capabilities and performance on various downstream Natural Language
Processing (NLP) tasks. While marveling at LLMs' incredible performance on all
kinds of tasks, we notice that they also have excellent multilingual processing
capabilities, such as Chinese. To explore the Chinese processing ability of
LLMs, we focus on Chinese Text Correction, a fundamental and challenging
Chinese NLP task. Specifically, we evaluate various representative LLMs on the
Chinese Grammatical Error Correction (CGEC) and Chinese Spelling Check (CSC)
tasks, which are two main Chinese Text Correction scenarios. Additionally, we
also fine-tune LLMs for Chinese Text Correction to better observe the potential
capabilities of LLMs. From extensive analyses and comparisons with previous
state-of-the-art small models, we empirically find that the LLMs currently have
both amazing performance and unsatisfactory behavior for Chinese Text
Correction. We believe our findings will promote the landing and application of
LLMs in the Chinese NLP community.
