Chinese Spelling Correction (CSC) aims to identify and correct character-level errors in Chinese text, where mistakes are predominantly caused by phonetic similarity and complex semantic ambiguity. Existing CSC approaches typically model phonetic and semantic information separately, which limits their ability to resolve errors requiring joint reasoning over pronunciation, tone, and global sentence meaning. In this paper, we propose a Phonetic–Semantic and Long–Short Information Fusion (PSIF) framework that explicitly integrates transliteration knowledge with sentence-level semantic representations. By incorporating tone-aware pinyin embeddings and fusing short-range phonetic features with long-range contextual semantics, PSIF effectively captures both local and global cues necessary for accurate correction. Extensive experiments on multiple CSC benchmarks demonstrate that the proposed method consistently outperforms state-of-the-art approaches, particularly on homophonic and context-sensitive errors. Furthermore, to investigate CSC under noisy input conditions in large language models (LLMs), we introduce UCMMLU, a novel benchmark constructed by injecting erroneous Chinese characters into CMMLU questions. Results show that applying PSIF as a preprocessing module significantly enhances LLM robustness and question-answering performance in zero-shot settings. These findings suggest that phonetic–semantic fusion not only advances CSC accuracy but also provides an effective pathway for improving the reliability of language models when handling misspelled or noisy Chinese text.
Zhang et al. (Tue,) studied this question.