TIPA and MTIPA fine-tune LLMs on reverse character-position prediction using the tokenizer's own vocabulary, improving Chinese spelling correction and character-level benchmarks.
A Comprehensive Evaluation and Analysis Study for Chinese Spelling Check
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abstract
With the development of pre-trained models and the incorporation of phonetic and graphic information, neural models have achieved high scores in Chinese Spelling Check (CSC). However, it does not provide a comprehensive reflection of the models' capability due to the limited test sets. In this study, we abstract the representative model paradigm, implement it with nine structures and experiment them on comprehensive test sets we constructed with different purposes. We perform a detailed analysis of the results and find that: 1) Fusing phonetic and graphic information reasonably is effective for CSC. 2) Models are sensitive to the error distribution of the test set, which reflects the shortcomings of models and reveals the direction we should work on. 3) Whether or not the errors and contexts have been seen has a significant impact on models. 4) The commonly used benchmark, SIGHAN, can not reliably evaluate models' performance.
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2024 1verdicts
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Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning
TIPA and MTIPA fine-tune LLMs on reverse character-position prediction using the tokenizer's own vocabulary, improving Chinese spelling correction and character-level benchmarks.