Large-scale language models (LLMs) has shown remarkable capability in various of Natural Language Processing (NLP) tasks and attracted lots of attention recently. However, some studies indicated that large language models fail to achieve promising result beyond the state-of-the-art models in English grammatical error correction (GEC) tasks. In this report, we aim to explore the how large language models perform on Chinese grammatical error correction tasks and provide guidance for future work. We conduct experiments with 3 different LLMs of different model scale on 4 Chinese GEC dataset. Our experimental results indicate that the performances of LLMs on automatic evaluation metrics falls short of the previous sota models because of the problem of over-correction. Furthermore, we also discover notable variations in the performance of LLMs when evaluated on different data distributions. Our findings demonstrates that further investigation is required for the application of LLMs on Chinese GEC task.

大规模语言模型在中文语法错误纠正任务中的表现及其问题的调查报告。研究发现大语言模型在自动评估指标方面表现不如之前的模型，并存在过度纠正的问题。此外，不同数据分布下大语言模型的表现也存在明显差异。这些发现表明需要进一步研究大语言模型在中文语法错误纠正任务上的应用。

评估大型语言模型在中文语法错误修正任务中的能力