Enhancing the Quality of Scientific Writing Using Advanced Language Models: Automated Evaluation and Proofreading | ||
| Journal of AI and Data Mining | ||
| دوره 13، شماره 1، فروردین 2025، صفحه 11-24 اصل مقاله (1.3 M) | ||
| نوع مقاله: Technical Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2025.15482.2661 | ||
| نویسندگان | ||
| Amir Ali Kharazmi؛ Hamid Hassanpour* | ||
| Faculty of Computer Engineering and IT, Shahrood University of Technology, Shahrood, Iran. | ||
| چکیده | ||
| Advancements in artificial intelligence have produced powerful language models that enhance scientific writing through automated evaluation and proofreading. Effective use of these models relies on prompt engineering—the precise formulation of requests—which directly influences output quality. As the saying goes, "Asking correctly is half of knowledge," emphasizing the importance of well-crafted prompts. In this study, we introduce a novel approach utilizing the simple language model Gemma-7b-it to improve scientific writing. By detailing the specific characteristics and structures of each section of a scientific paper, we prompt the model to evaluate and proofread text for clarity, coherence, and adherence to academic standards. Our method comprises three stages: initial evaluation, feedback-based proofreading, and iterative refinement using textual gradient optimization. Tested on a dataset of 25 scientific articles, expert evaluations confirm that this method achieves significant enhancements in abstract quality. These findings demonstrate that meticulous prompt engineering can enable simpler language models to produce results comparable to advanced models like GPT-4, underscoring the critical role of prompt optimization in achieving high-quality scientific writing. | ||
| کلیدواژهها | ||
| Artificial Intelligence؛ Natural Language Processing؛ Prompt Optimization؛ Gradient-based Optimization | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 205 تعداد دریافت فایل اصل مقاله: 232 |
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