LLMCL-GEC: Advancing grammatical error correction with LLM-driven curriculum learning

While large-scale language models (LLMs) have demonstrated remarkable capabilities in specific natural language processing (NLP) tasks, they may still lack proficiency compared to specialized models in certain domains, such as grammatical error correction (GEC). Drawing inspiration from the concept of curriculum learning, we have delved into refining LLMs into proficient GEC experts by devising effective curriculum learning (CL) strategies. In this paper, we introduce a novel approach, termed LLM-based curriculum learning, which capitalizes on the robust semantic comprehension and discriminative prowess inherent in LLMs to gauge the complexity of GEC training data. Unlike traditional curriculum learning techniques, our method closely mirrors human expert-designed curriculums. Leveraging the proposed LLM-based CL method, we sequentially select varying levels of curriculums ranging from easy to hard, and iteratively train and refine using the pretrianed T5 and LLaMA series models. Through rigorous testing and analysis across diverse benchmark assessments in English GEC, including the CoNLL14 test, BEA19 test, and BEA19 development sets, our approach showcases a significant performance boost over baseline models and conventional curriculum learning methodologies. Specifically, our method achieves a new state-of-the-art (SOTA) result on the CoNLL14 test set, with an F 0 . 5 score of 69.6. Additionally, on the BEA19 test set and BEA19 development set, our approach outperforms conventional curriculum learning methodologies by 1.0 and 0.3 F 0 . 5 points, respectively. • This paper applies curriculum learning paradigm to GEC, offering a novel perspective. • It confirms LLMs can reduce the need for human data annotation and selection tasks. • Presents an LLM-driven learning approach, using models like LLaMA2-70b and GPT-4, to boost GEC performance. • Includes extensive experiments, validating the method against English benchmarks. • Features an ablation study focusing on new baselines and learning guidelines.