CEC-Zero: Zero-Supervision Character Error Correction with Self-Generated Rewards
This addresses the need for robust and scalable Chinese spelling correction in real-world text processing without costly annotations, representing a novel paradigm rather than an incremental improvement.
The paper tackles the problem of Chinese spelling correction without supervision by introducing CEC-Zero, a reinforcement learning framework that uses self-generated rewards to enable LLMs to correct their own mistakes, achieving 10-13 F1 point improvements over supervised baselines and 5-8 points over fine-tuned LLMs across 9 benchmarks.
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 zero-supervision 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 F$_1$ 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.