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.- Anthology ID:
- 2024.bea-1.6
- Volume:
- Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Ekaterina Kochmar, Marie Bexte, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Anaïs Tack, Victoria Yaneva, Zheng Yuan
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 68–77
- Language:
- URL:
- https://aclanthology.org/2024.bea-1.6
- DOI:
- Cite (ACL):
- Masamune Kobayashi, Masato Mita, and Mamoru Komachi. 2024. Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 68–77, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Large Language Models Are State-of-the-Art Evaluator for Grammatical Error Correction (Kobayashi et al., BEA 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.bea-1.6.pdf