نوع مقاله : مقاله پژوهشی
نویسندگان
1 کارشناسی ارشد، گروه مهندسی سیستم‌های هوشمند، دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران.
2 استاد، گروه مهندسی سیستم‌های هوشمند، دانشکده مهندسی صنایع، دانشگاه علم و صنعت ایران، تهران، ایران.
3 استادیار، گروه مترجمی زبان انگلیسی، دانشکده علوم انسانی، دانشگاه دامغان، دامغان، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
This study aims to examine the application of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) in accurately extracting information from applied linguistics research articles. With the growing volume of publications, the need for automated tools to transform unstructured texts into analyzable data has become increasingly urgent. Through a systematic literature review, this research proposes a conceptual framework based on LLM and RAG to extract components such as research questions, theoretical frameworks, methodologies, findings, and limitations. The methodology involves selecting articles from secondary databases, designing specialized prompts, and conducting evaluations using Precision, Recall, and F1-Score metrics. Findings indicate that the integration of LLM and RAG achieves high accuracy (average F1 = 0.81) in extracting structured elements such as data sources and analytical methods, while inferential components still require human validation. These results highlight the significant potential of this approach for accelerating systematic literature reviews and offer practical recommendations, such as fine-tuning, to enhance overall performance.
کلیدواژه‌ها [English]
)