Rich Semantic Knowledge Enhanced Large Language Models for Few-shot
  Chinese Spell Checking
Ming Dong Yujing Chen Miao Zhang Hao Sun Tingting He
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.