Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction
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| Format: | Preprint |
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2024
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| _version_ | 1866913288369995776 |
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| author | Tang, Chenming Qu, Fanyi Wu, Yunfang |
| author_facet | Tang, Chenming Qu, Fanyi Wu, Yunfang |
| contents | In the era of large language models (LLMs), in-context learning (ICL) stands out as an effective prompting strategy that explores LLMs' potency across various tasks. However, applying LLMs to grammatical error correction (GEC) is still a challenging task. In this paper, we propose a novel ungrammatical-syntax-based in-context example selection strategy for GEC. Specifically, we measure similarity of sentences based on their syntactic structures with diverse algorithms, and identify optimal ICL examples sharing the most similar ill-formed syntax to the test input. Additionally, we carry out a two-stage process to further improve the quality of selection results. On benchmark English GEC datasets, empirical results show that our proposed ungrammatical-syntax-based strategies outperform commonly-used word-matching or semantics-based methods with multiple LLMs. This indicates that for a syntax-oriented task like GEC, paying more attention to syntactic information can effectively boost LLMs' performance. Our code will be publicly available after the publication of this paper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19283 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction Tang, Chenming Qu, Fanyi Wu, Yunfang Computation and Language In the era of large language models (LLMs), in-context learning (ICL) stands out as an effective prompting strategy that explores LLMs' potency across various tasks. However, applying LLMs to grammatical error correction (GEC) is still a challenging task. In this paper, we propose a novel ungrammatical-syntax-based in-context example selection strategy for GEC. Specifically, we measure similarity of sentences based on their syntactic structures with diverse algorithms, and identify optimal ICL examples sharing the most similar ill-formed syntax to the test input. Additionally, we carry out a two-stage process to further improve the quality of selection results. On benchmark English GEC datasets, empirical results show that our proposed ungrammatical-syntax-based strategies outperform commonly-used word-matching or semantics-based methods with multiple LLMs. This indicates that for a syntax-oriented task like GEC, paying more attention to syntactic information can effectively boost LLMs' performance. Our code will be publicly available after the publication of this paper. |
| title | Ungrammatical-syntax-based In-context Example Selection for Grammatical Error Correction |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.19283 |