Edit-Wise Preference Optimization for Grammatical Error Correction

Abstract

While large language models (LLMs) have achieved remarkable success in various natural language processing tasks, their strengths have yet to be fully demonstrated in grammatical error correction (GEC). This is partly due to the misalignment between their pre-training objectives and the GEC principle of making minimal edits. In this work, we aim to bridge this gap by introducing a novel method called Edit-wise Preference Optimization (EPO). By distinguishing the importance of different tokens and assigning higher reward weights to edit tokens during preference optimization, our method captures fine-grained distinctions in GEC that traditional preference learning often overlooks. Extensive experiments on both English and Chinese datasets show that our framework consistently outperforms strong baselines, achieving state-of-the-art performance and demonstrating the advantages of LLMs in GEC.