Rich Semantic Knowledge Enhanced Large Language Models for Few-shot Chinese Spell Checking
This addresses a limitation in practical applications like speech-to-text and OCR for Chinese language processing, though it appears incremental as it builds on existing in-context learning methods.
The paper tackles the problem of Chinese Spell Checking (CSC) in few-shot scenarios by introducing large language models (LLMs) enhanced with rich semantic knowledge, achieving better performance than BERT-based models.
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.