Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction
This addresses the need for better automatic evaluation metrics in GEC, which is important for language processing applications, but it is incremental as it applies an existing LLM approach to a new task.
The study tackled the problem of evaluating grammatical error correction (GEC) by using large language models (LLMs) as evaluators, and found that GPT-4 achieved a Kendall's rank correlation of 0.662 with human judgments, surpassing all existing methods.
Large Language Models (LLMs) have been reported to outperform existing automatic evaluation metrics in some tasks, such as text summarization and machine translation. However, there has been a lack of research on LLMs as evaluators in grammatical error correction (GEC). In this study, we investigate the performance of LLMs in GEC evaluation by employing prompts designed to incorporate various evaluation criteria inspired by previous research. Our extensive experimental results demonstrate that GPT-4 achieved Kendall's rank correlation of 0.662 with human judgments, surpassing all existing methods. Furthermore, in recent GEC evaluations, we have underscored the significance of the LLMs scale and particularly emphasized the importance of fluency among evaluation criteria.