Prompting open-source and commercial language models for grammatical
error correction of English learner text
error correction of English learner text
File(s)
Preprint version
OA Location
Author(s)
Type
preprint
Abstract
Thanks to recent advances in generative AI, we are able to prompt large language models (LLMs) to produce texts which are fluent and grammatical. In addition, it has been shown that we can elicit attempts at grammatical error correction (GEC) from LLMs when prompted with ungrammatical input sentences. We evaluate how well LLMs can perform at GEC by measuring their performance on established benchmark datasets. We go beyond previous studies, which only examined GPT * models on a selection of English GEC datasets, by evaluating seven open-source and three commercial LLMs on four established GEC benchmarks. We investigate model performance and report results against individual error types. Our results indicate that LLMs do not always outperform supervised English GEC models except in specific contexts-namely commercial LLMs on benchmarks annotated with fluency corrections as opposed to minimal edits. We find that several open-source models outperform commercial ones on minimal edit benchmarks, and that in some settings zero-shot prompting is just as competitive as few-shot prompting.
Date Issued
2024-01-15
Citation
arXiv, 2024
Journal / Book Title
arXiv
Copyright Statement
Copyright © 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 International License.
License URL
Description
Preprint version
Identifier
http://arxiv.org/abs/2401.07702v2
Subjects
cs.CL
cs.CL