CEC-Zero: Zero-Supervision Character Error Correction with Self-Generated Rewards

Large-scale Chinese spelling correction (CSC) remains critical for real-world text processing, yet existing LLMs and supervised methods lack robustness to novel errors and rely on costly annotations. We introduce CEC-Zero, a zerosupervision reinforcement learning framework that addresses this by enabling LLMs to correct their own mistakes. CEC-Zero synthesizes errorful inputs from clean text, computes cluster-consensus rewards via semantic similarity and candidate agreement, and optimizes the policy with PPO. It outperforms supervised baselines by 10–13 F1 points and strong LLM fine-tunes by 5–8 points across 9 benchmarks, with theoretical guarantees of unbiased rewards and convergence.CEC-Zero establishes a label-free paradigm for robust, scalable CSC, unlocking LLM potential in noisy text pipelines.

Authors

• Zhiming Lin Nankai University
• Kai Zhao Nanyang Technological University
• Sophie Zhang Shanghai High School International Division
• Peilai Yu
• Canran Xiao Shenzhen Campus of Sun Yat-sen University

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