CEC-Zero: Chinese Error Correction Solution Based on LLM

• URL: http://arxiv.org/abs/2505.09082v1
• Date: Wed, 14 May 2025 02:35:47 GMT
• Title: CEC-Zero: Chinese Error Correction Solution Based on LLM
• Authors: Sophie Zhang, Zhiming Lin,
• Abstract summary: Recent advancements in large language models (LLMs) demonstrate exceptional Chinese text processing capabilities.<n>This paper proposes CEC-Zero, a novel reinforcement learning framework enabling LLMs to self-correct.<n> Experiments reveal RL-enhanced LLMs achieve industry-viable accuracy and superior cross-domain generalization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in large language models (LLMs) demonstrate exceptional Chinese text processing capabilities, particularly in Chinese Spelling Correction (CSC). While LLMs outperform traditional BERT-based models in accuracy and robustness, challenges persist in reliability and generalization. This paper proposes CEC-Zero, a novel reinforcement learning (RL) framework enabling LLMs to self-correct through autonomous error strategy learning without external supervision. By integrating RL with LLMs' generative power, the method eliminates dependency on annotated data or auxiliary models. Experiments reveal RL-enhanced LLMs achieve industry-viable accuracy and superior cross-domain generalization, offering a scalable solution for reliability optimization in Chinese NLP applications. This breakthrough facilitates LLM deployment in practical Chinese text correction scenarios while establishing a new paradigm for self-improving language models.

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