  Chinese Spell Checking (CSC) aims to detect and correct spelling errors in
sentences. Despite Large Language Models (LLMs) exhibit robust capabilities and
are widely applied in various tasks, their performance on CSC is often
unsatisfactory. We find that LLMs fail to meet the Chinese character-level
constraints of the CSC task, namely equal length and phonetic similarity,
leading to a performance bottleneck. Further analysis reveal that this issue
stems from the granularity of tokenization, as current mixed character-word
tokenization struggles to satisfy these character-level constraints. To address
this issue, we propose C-LLM, a Large Language Model-based Chinese Spell
Checking method that learns to check errors Character by Character.
Character-level tokenization enables the model to learn character-level
alignment, effectively mitigating issues related to character-level
constraints. Furthermore, CSC is simplified to replication-dominated and
substitution-supplemented tasks. Experiments on two CSC benchmarks demonstrate
that C-LLM achieves an average improvement of 10% over existing methods.
Specifically, it shows a 2.1% improvement in general scenarios and a
significant 12% improvement in vertical domain scenarios, establishing
state-of-the-art performance. The source code can be accessed at
https://github.com/ktlKTL/C-LLM.
