Chinese Spelling Correction: A Comprehensive Survey of Progress, Challenges, and Opportunities

TL;DR

This survey reviews the evolution, challenges, and future opportunities in Chinese Spelling Correction, emphasizing the transition from pre-trained models to large language models and their potential for improved accuracy.

Contribution

It is the first comprehensive survey on CSC, analyzing existing datasets, challenges, and proposing future research directions involving large language models.

Findings

Analysis of strengths and weaknesses of pre-trained and large language models in CSC

Identification of dataset limitations and challenges in CSC

Proposed future research directions leveraging LLMs' reasoning capabilities

Abstract

Chinese Spelling Correction (CSC) is a critical task in natural language processing, aimed at detecting and correcting spelling errors in Chinese text. This survey provides a comprehensive overview of CSC, tracing its evolution from pre-trained language models to large language models, and critically analyzing their respective strengths and weaknesses in this domain. Moreover, we further present a detailed examination of existing benchmark datasets, highlighting their inherent challenges and limitations. Finally, we propose promising future research directions, particularly focusing on leveraging the potential of LLMs and their reasoning capabilities for improved CSC performance. To the best of our knowledge, this is the first comprehensive survey dedicated to the field of CSC. We believe this work will serve as a valuable resource for researchers, fostering a deeper understanding of the field and inspiring future advancements.