Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913244107505664 |
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| author | Zeng, Min Kuang, Jiexin Qiu, Mengyang Song, Jayoung Park, Jungyeul |
| author_facet | Zeng, Min Kuang, Jiexin Qiu, Mengyang Song, Jayoung Park, Jungyeul |
| contents | The writing examples of English language learners may be different from those of native speakers. Given that there is a significant differences in second language (L2) learners' error types by their proficiency levels, this paper attempts to reduce overcorrection by examining the interaction between LLM's performance and L2 language proficiency. Our method focuses on zero-shot and few-shot prompting and fine-tuning models for GEC for learners of English as a foreign language based on the different proficiency. We investigate GEC results and find that overcorrection happens primarily in advanced language learners' writing (proficiency C) rather than proficiency A (a beginner level) and proficiency B (an intermediate level). Fine-tuned LLMs, and even few-shot prompting with writing examples of English learners, actually tend to exhibit decreased recall measures. To make our claim concrete, we conduct a comprehensive examination of GEC outcomes and their evaluation results based on language proficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15930 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency Zeng, Min Kuang, Jiexin Qiu, Mengyang Song, Jayoung Park, Jungyeul Computation and Language The writing examples of English language learners may be different from those of native speakers. Given that there is a significant differences in second language (L2) learners' error types by their proficiency levels, this paper attempts to reduce overcorrection by examining the interaction between LLM's performance and L2 language proficiency. Our method focuses on zero-shot and few-shot prompting and fine-tuning models for GEC for learners of English as a foreign language based on the different proficiency. We investigate GEC results and find that overcorrection happens primarily in advanced language learners' writing (proficiency C) rather than proficiency A (a beginner level) and proficiency B (an intermediate level). Fine-tuned LLMs, and even few-shot prompting with writing examples of English learners, actually tend to exhibit decreased recall measures. To make our claim concrete, we conduct a comprehensive examination of GEC outcomes and their evaluation results based on language proficiency. |
| title | Evaluating Prompting Strategies for Grammatical Error Correction Based on Language Proficiency |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.15930 |