CEC-Zero: Chinese Error Correction Solution Based on LLM
This provides a scalable solution for reliability optimization in Chinese NLP applications, facilitating LLM deployment in practical text correction scenarios.
The paper tackles the problem of improving reliability and generalization in Chinese Spelling Correction (CSC) by proposing CEC-Zero, a reinforcement learning framework that enables large language models (LLMs) to self-correct without external supervision. The result shows that RL-enhanced LLMs achieve industry-viable accuracy and superior cross-domain generalization.
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.