LLMCL-GEC: Advancing Grammatical Error Correction with LLM-Driven Curriculum Learning

TL;DR

This paper introduces LLMCL-GEC, a novel curriculum learning approach that leverages large language models to improve grammatical error correction by mimicking human-like curriculum strategies, resulting in significant performance gains.

Contribution

It proposes a new LLM-based curriculum learning method for GEC, utilizing LLMs to assess data complexity and enhance training effectiveness over traditional methods.

Findings

Significant performance improvements on GEC benchmarks.

Effective data complexity assessment using LLMs.

Outperforms baseline and traditional curriculum learning methods.

Abstract

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.