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To address the limitations of monolithic analysis and uncontrollable revisions in traditional Chinese grammatical error correction (CGEC) methods, this paper proposes a multi - agent collaborative CGEC framework with three core capabilities—memory capability, execution capability, and planning capability. Driven by large language models (LLMs), the framework functions via: (1) the Tokenization Agent generating structured part-of-speech sequences as semantic primitives; (2) the Syntax Validation Agent and Semantic Verification Agent conducting parallel formal rule detection and logical-semantic verification to identify conflicts and inconsistencies; (3) the Error Classification Agent integrating multi-source features for dynamic error classification and precise localization with linguistically logical labels; and (4) the Error Correction Agent adhering to the minimal intervention principle to generate and select optimal revision plans via LLMs and a three-dimensional evaluation model. Experiments show the framework significantly boosts error correction performance, with MACGEC achieving an F0.5 of 44.13 on NaCGEC, outperforming DeepSeek-R1 32B COT (29.96) by 47.3%. In time efficiency, MACGEC processes in 98s, 15.5% faster than COT’s 116s, while maintaining higher accuracy. This demonstrates its advantages in detection, flexibility, revision quality, and real-time performance, with modular and adaptive capabilities.

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Large Language Model-Driven Multi-agent Collaborative Framework for Chinese Grammatical Error Correction

  • 会议论文

  • 发表日期：

    2026年

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  • Xiaoqiang Wang,
  • Chengrui Qi,
  • Nier Wu

摘要

To address the limitations of monolithic analysis and uncontrollable revisions in traditional Chinese grammatical error correction (CGEC) methods, this paper proposes a multi - agent collaborative CGEC framework with three core capabilities—memory capability, execution capability, and planning capability. Driven by large language models (LLMs), the framework functions via: (1) the Tokenization Agent generating structured part-of-speech sequences as semantic primitives; (2) the Syntax Validation Agent and Semantic Verification Agent conducting parallel formal rule detection and logical-semantic verification to identify conflicts and inconsistencies; (3) the Error Classification Agent integrating multi-source features for dynamic error classification and precise localization with linguistically logical labels; and (4) the Error Correction Agent adhering to the minimal intervention principle to generate and select optimal revision plans via LLMs and a three-dimensional evaluation model. Experiments show the framework significantly boosts error correction performance, with MACGEC achieving an F0.5 of 44.13 on NaCGEC, outperforming DeepSeek-R1 32B COT (29.96) by 47.3%. In time efficiency, MACGEC processes in 98s, 15.5% faster than COT’s 116s, while maintaining higher accuracy. This demonstrates its advantages in detection, flexibility, revision quality, and real-time performance, with modular and adaptive capabilities.