GEE! Grammar Error Explanation with Large Language Models

Yixiao Song, Kalpesh Krishna, Rajesh Bhatt, Kevin Gimpel, Mohit Iyyer


Abstract
Existing grammatical error correction tools do not provide natural language explanations of the errors that they correct in user-written text. However, such explanations are essential for helping users learn the language by gaining a deeper understanding of its grammatical rules (DeKeyser, 2003; Ellis et al., 2006).To address this gap, we propose the task of grammar error explanation, where a system needs to provide one-sentence explanations for each grammatical error in a pair of erroneous and corrected sentences. The task is not easily solved by prompting LLMs: we find that, using one-shot prompting, GPT-4 only explains 40.6% of the errors and does not even attempt to explain 39.8% of the errors.Since LLMs struggle to identify grammar errors, we develop a two-step pipeline that leverages fine-tuned and prompted large language models to perform structured atomic token edit extraction, followed by prompting GPT-4 to explain each edit. We evaluate our pipeline on German, Chinese, and English grammar error correction data. Our atomic edit extraction achieves an F1 of 0.93 on German, 0.91 on Chinese, and 0.891 on English. Human evaluation of generated explanations reveals that 93.9% of German errors, 96.4% of Chinese errors, and 92.20% of English errors are correctly detected and explained. To encourage further research, we open-source our data and code.
Anthology ID:
2024.findings-naacl.49
Volume:
Findings of the Association for Computational Linguistics: NAACL 2024
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
754–781
Language:
URL:
https://aclanthology.org/2024.findings-naacl.49
DOI:
Bibkey:
Cite (ACL):
Yixiao Song, Kalpesh Krishna, Rajesh Bhatt, Kevin Gimpel, and Mohit Iyyer. 2024. GEE! Grammar Error Explanation with Large Language Models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 754–781, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
GEE! Grammar Error Explanation with Large Language Models (Song et al., Findings 2024)
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https://aclanthology.org/2024.findings-naacl.49.pdf
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