Exploring sentence-level revision capabilities of large language models in English for academic purposes writing assistance
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| Pubblicato in: | Asian-Pacific Journal of Second and Foreign Language Education vol. 10, no. 1 (Dec 2025), p. 27 |
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| Pubblicazione: |
Springer Nature B.V.
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| Soggetti: | |
| Accesso online: | Citation/Abstract Full Text - PDF |
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| 024 | 7 | |a 10.1186/s40862-025-00334-z |2 doi | |
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| 045 | 2 | |b d20251201 |b d20251231 | |
| 245 | 1 | |a Exploring sentence-level revision capabilities of large language models in English for academic purposes writing assistance | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The English for Academic Purposes (EAP) is pivotal for scholarly communication; however, it poses significant challenges for non-native English speakers. Recently, Large Language Models (LLMs) have been extensively utilized in EAP to assist with writing tasks. EAP writing assistance typically encompasses several downstream tasks in natural language processing, such as Grammatical Error Correction (GEC). Nonetheless, some studies have revealed that the performance of LLMs in GEC tasks is inferior to traditional GEC solutions. To explore the capabilities of LLMs more thoroughly in aspects like deep semantic and syntactic structures, this study aims to rigorously assess the performance of LLMs in the Sentence-level Revision (SentRev) task. We designed three sets of meticulous experiments to evaluate the efficacy of different LLMs. The first experiment assessed LLMs using prompts in ten different languages, finding that the SentRev performance of LLMs was heavily influenced by the language of the prompt and the quality of the input text. The second experiment investigated the performance of English LLMs with minimal prompting in the SentRev task, yet the results showed no significant changes, contradicting some prior studies. In the third experiment, we devised an innovative and straightforward method that significantly enhanced the performance of multiple LLMs by integrating academic phrases from the Formulaic Language Academic Phrasebank (<ext-link xlink:href="https://www.phrasebank.manchester.ac.uk/" ext-link-type="uri">https://www.phrasebank.manchester.ac.uk/</ext-link>), thus overcoming the performance limitations imposed by different languages on LLMs. Additionally, our study highlights the deficiencies in existing evaluation benchmarks and suggests that higher-level, discourse-based EAP text evaluation benchmarks merit deeper exploration. | |
| 653 | |a Language | ||
| 653 | |a Semantics | ||
| 653 | |a Syntax | ||
| 653 | |a Syntactic structures | ||
| 653 | |a Natural language processing | ||
| 653 | |a Academic writing | ||
| 653 | |a Academic discourse | ||
| 653 | |a Language modeling | ||
| 653 | |a Large language models | ||
| 653 | |a Scholarly communication | ||
| 653 | |a Formulaic language | ||
| 653 | |a English for academic purposes | ||
| 653 | |a Second language learning | ||
| 653 | |a Experiments | ||
| 653 | |a Writing | ||
| 653 | |a English language | ||
| 653 | |a Task performance | ||
| 653 | |a Efficacy | ||
| 653 | |a Languages | ||
| 653 | |a Academic achievement | ||
| 653 | |a Language Processing | ||
| 773 | 0 | |t Asian-Pacific Journal of Second and Foreign Language Education |g vol. 10, no. 1 (Dec 2025), p. 27 | |
| 786 | 0 | |d ProQuest |t Education Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3212989461/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3212989461/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |