Mixture of Small and Large Models for Chinese Spelling Check

• URL: http://arxiv.org/abs/2506.06887v1
• Date: Sat, 07 Jun 2025 18:29:10 GMT
• Title: Mixture of Small and Large Models for Chinese Spelling Check
• Authors: Ziheng Qiao, Houquan Zhou, Zhenghua Li,
• Abstract summary: In the era of large language models (LLMs), the Chinese Spelling Check (CSC) task has seen various LLM methods developed.<n>Fine-tuned BERT-based models, relying on high-quality in-domain data, show excellent performance but suffer from edit pattern overfitting.<n>This paper proposes a novel dynamic mixture approach that effectively combines the probability distributions of small models and LLMs during the beam search decoding phase.
• Score: 10.634101727583127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the era of large language models (LLMs), the Chinese Spelling Check (CSC) task has seen various LLM methods developed, yet their performance remains unsatisfactory. In contrast, fine-tuned BERT-based models, relying on high-quality in-domain data, show excellent performance but suffer from edit pattern overfitting. This paper proposes a novel dynamic mixture approach that effectively combines the probability distributions of small models and LLMs during the beam search decoding phase, achieving a balanced enhancement of precise corrections from small models and the fluency of LLMs. This approach also eliminates the need for fine-tuning LLMs, saving significant time and resources, and facilitating domain adaptation. Comprehensive experiments demonstrate that our mixture approach significantly boosts error correction capabilities, achieving state-of-the-art results across multiple datasets. Our code is available at https://github.com/zhqiao-nlp/MSLLM.

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