Harnessing Rule-Based Reinforcement Learning for Enhanced Grammatical Error Correction

2025-08-26

AI Summary

To address the limitations of large language models (LLMs) in grammatical error correction—including heavy reliance on supervised fine-tuning, constrained reasoning capacity, and opaque, uncontrollable correction processes—this paper proposes a rule-guided reinforcement learning (RL) framework. Our method encodes domain-specific grammatical rules as differentiable constraints and integrates them into the RL training pipeline of an encoder-decoder architecture, optimizing correction policies via policy gradients rather than end-to-end generation. The key contributions are: (i) the first integration of explicit syntactic/grammatical rules with RL for grammatical error correction, enabling controllable inference and interpretable correction logic; and (ii) substantial improvements in recall for Chinese grammatical error correction, achieving state-of-the-art performance across multiple benchmark datasets. These results empirically validate that rule-guided RL effectively unlocks LLMs’ latent deep reasoning capabilities for structured linguistic tasks.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Grammatical error correction is a significant task in NLP. Traditional methods based on encoder-decoder models have achieved certain success, but the application of LLMs in this field is still underexplored. Current research predominantly relies on supervised fine-tuning to train LLMs to directly generate the corrected sentence, which limits the model's powerful reasoning ability. To address this limitation, we propose a novel framework based on Rule-Based RL. Through experiments on the Chinese datasets, our Rule-Based RL framework achieves extbf{state-of-the-art }performance, with a notable increase in extbf{recall}. This result clearly highlights the advantages of using RL to steer LLMs, offering a more controllable and reliable paradigm for future development in GEC.

Problem

• Enhancing grammatical error correction using rule-based reinforcement learning
• Addressing limitations of supervised fine-tuning in LLMs
• Improving recall and controllability in error correction systems

Innovation

• Rule-Based Reinforcement Learning framework
• Enhances LLMs reasoning via RL steering
• Achieves state-of-the-art recall performance