Chinese Spell Checking (CSC) is a widely used technology, which plays a vital role in speech to text (STT) and optical character recognition (OCR). Most of the existing CSC approaches relying on BERT architecture achieve excellent performance. However, limited by the scale of the foundation model, BERT-based method does not work well in few-shot scenarios, showing certain limitations in practical applications. In this paper, we explore using an in-context learning method named RS-LLM (Rich Semantic based LLMs) to introduce large language models (LLMs) as the foundation model. Besides, we study the impact of introducing various Chinese rich semantic information in our framework. We found that by introducing a small number of specific Chinese rich semantic structures, LLMs achieve better performance than the BERT-based model on few-shot CSC task. Furthermore, we conduct experiments on multiple datasets, and the experimental results verified the superiority of our proposed framework.

翻译：中文拼写检查是一项广泛应用的技术，在语音转文本和光学字符识别中发挥着至关重要的作用。现有大多数基于BERT架构的中文拼写检查方法已取得优异性能。然而，受限于基础模型的规模，基于BERT的方法在少样本场景中表现欠佳，在实际应用中存在一定局限性。本文探索采用一种名为RS-LLM（基于丰富语义的大型语言模型）的上下文学习方法，引入大型语言模型作为基础模型。此外，我们研究了在框架中引入多种中文丰富语义信息的影响。研究发现，通过引入少量特定的中文丰富语义结构，大型语言模型在少样本中文拼写检查任务上取得了优于基于BERT模型的性能。我们在多个数据集上进行了实验，实验结果验证了所提出框架的优越性。