Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction

• URL: http://arxiv.org/abs/2403.17540v2
• Date: Sun, 26 May 2024 11:55:11 GMT
• Title: Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction
• Authors: Masamune Kobayashi, Masato Mita, Mamoru Komachi,
• Abstract summary: Large Language Models (LLMs) have been reported to outperform existing automatic evaluation metrics in some tasks. This study investigates the performance of LLMs in grammatical error correction (GEC) evaluation by employing prompts inspired by previous research.
• Score: 14.822205658480813
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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