  Recently, Large Language Models (LLMs) have been widely studied by
researchers for their roles in various downstream NLP tasks. As a fundamental
task in the NLP field, Chinese Grammatical Error Correction (CGEC) aims to
correct all potential grammatical errors in the input sentences. Previous
studies have shown that LLMs' performance as correctors on CGEC remains
unsatisfactory due to its challenging task focus. To promote the CGEC field to
better adapt to the era of LLMs, we rethink the roles of LLMs in the CGEC task
so that they can be better utilized and explored in CGEC. Considering the rich
grammatical knowledge stored in LLMs and their powerful semantic understanding
capabilities, we utilize LLMs as explainers to provide explanation information
for the CGEC small models during error correction to enhance performance. We
also use LLMs as evaluators to bring more reasonable CGEC evaluations, thus
alleviating the troubles caused by the subjectivity of the CGEC task. In
particular, our work is also an active exploration of how LLMs and small models
better collaborate in downstream tasks. Extensive experiments and detailed
analyses on widely used datasets verify the effectiveness of our thinking
intuition and the proposed methods.
