C - LLM : Learn to Check C hinese Spelling Errors Character by Character (2024)

Abstract: 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 reveals 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 a 2.1% enhancement in general scenarios and a significant 12% improvement in vertical domain scenarios compared to existing methods, establishing state-of-the-art performance.

## introduction chinese spell checking (csc) involves detecting and correcting erroneous characters in chinese sentences, playing a vital role in applications @xcite . although large language models (llms) exhibit potent capabilities and are increasingly being applied to a variety of tasks @xcite ; wu 有 胆(dan) 量 的 图片 曝光 有 大(da)量的 图片 曝光 pictures with courage exposed there are a lot of pictures exposed tokens of source (6 tokens) 有 胆 量 的 图 片 曝 光 有 大 量 的 图 片 曝 光 tokens of source (8 tokens) tokens of reference (4 tokens) tokens of reference (8 tokens) ## methodology the csc task requires a character-level mapping, necessitating character-by-character correction rather than token-by-token. since current llms process sentences at the token level, mapping each character to a token can intuitively reduce the complexity of csc for llms. based on this concept, we propose c-llm (as shown in figure 3 ), a large language model-based chinese spell checking method that learns to check errors character by character. this approach consists of three main steps, as detailed below. ## experiments in this section, we present the details of fine-tuning and the evaluation results of models on the two csc benchmarks: the general dataset cscd-ns and the multi-domain dataset lemon. ## conclusion this paper indicates that llms fail to meet the chinese character-level constraints of the csc task, namely equal length and phonetic similarity, which hinders their correction performance. we find that the root cause lies in the granularity of tokeniza-tion, which mixes characters and words, making it difficult to satisfy these character-level constraints. to address this issue, we propose c-llm, which establishes mappings between chinese characters, enabling the model to learn correction relationships and phonetic similarities. this approach simplifies the csc task to character replication and substitution. experimental results demonstrate that c-llm outperforms previous methods on both general and multi-domain benchmarks, achieving state-of-theart performance.