Papers with English GEC datasets
Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction (2024.naacl-long)
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| Challenge: | In-context learning (ICL) has shown impressive results on many tasks, but applying LLMs to grammatical error correction (GEC) is still a challenging task. |
| Approach: | They propose an ungrammatical-syntax-based in-context example selection strategy that measures similarity of sentences based on their syntactic structures and identify optimal ICL examples sharing the most similar ill-formed syntax to the test input. |
| Outcome: | The proposed strategy outperforms word-matching and semantics-based methods on a syntax-oriented task like GEC on benchmark English datasets. |
Prompting open-source and commercial language models for grammatical error correction of English learner text (2024.findings-acl)
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Christopher Davis, Andrew Caines, O Andersen, Shiva Taslimipoor, Helen Yannakoudakis, Zheng Yuan, Christopher Bryant, Marek Rei, Paula Buttery
| Challenge: | Recent advances in generative AI have enabled us to prompt large language models (LLMs) to produce texts which are fluent and grammatical. |
| Approach: | They evaluate model performance by measuring their performance on established benchmarks. |
| Outcome: | The proposed models outperform supervised English GEC models on fluency correction benchmarks and commercial LLMs on edit benchmarks. |