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.

翻译：近期，大语言模型的开发与进步令整个人工智能界为之惊叹。凭借其涌现能力，大语言模型吸引了越来越多研究者探究其在各类下游自然语言处理任务中的能力与表现。在惊叹于大语言模型在各种任务上的卓越表现之余，我们注意到它们还具备出色的多语言处理能力，例如中文处理能力。为探索大语言模型的中文处理能力，我们聚焦于中文文本纠正这一基础且具有挑战性的中文自然语言处理任务。具体而言，我们评估了多种代表性大语言模型在中文语法纠错和中文拼写检查这两项主要中文文本纠正场景中的表现。此外，我们还对大语言模型进行微调以用于中文文本纠正，从而更好地观察其潜在能力。通过广泛的分析并与先前最先进的小型模型进行对比，我们基于实验发现，目前大语言模型在中文文本纠正任务中既展现出惊人表现，也存在不尽如人意的行为。我们相信，这些发现将推动大语言模型在中文自然语言处理领域的落地与应用。