让大模型自己学会纠错，不用人工标注数据
CEC-Zero: Chinese Error Correction Solution Based on LLM
- 用强化学习让大模型自主学习纠错策略
- 在多个中文语料上达到工业级准确率
- 适合需要高可靠性中文文本纠错的场景
大型语言模型（LLMs）在中文文本处理方面表现出色，尤其在中文拼写纠错（CSC）任务中。尽管其准确性和鲁棒性优于传统BERT模型，但仍存在可靠性与泛化能力不足的问题。本文提出CEC-Zero，一种基于强化学习（RL）的新框架，使大模型能通过自主学习纠错策略实现自我修正，无需外部监督。该方法结合强化学习与大模型的生成能力，摆脱了对标注数据或辅助模型的依赖。实验表明，增强后的模型在多个数据集上达到工业可用的准确率，并展现出优异的跨领域泛化能力，为中文自然语言处理中的可靠性优化提供可扩展解决方案。该突破推动大模型在实际中文文本纠错场景中的应用，同时建立了一种自提升语言模型的新范式。
原文摘要 · Abstract (English)
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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