Shahrood University of Technology Journal of AI and Data Mining 2322-5211 Articles in Press 2026 07 18 Named Entity Recognition from Official Texts Based on Multi-Agent Architecture in Large Language Models 3853 10.22044/jadm.2026.16663.2804 EN Mohammad Aalishahi Instructor, Department of Computer Science, Kish International Campus, University of Tehran, Kish, Iran Mohammad Hadi Bokaei ICT Research Institute Tehran, Iran. Abolfazl Nadi Computer Science, Tehran, Kish, Iran Journal Article 2025 08 25 Given the importance of Named Entity Recognition (NER), numerous studies have been conducted in this field. However, most research has focused on languages such as English, French, and Arabic. In contrast, studies on Persian remain limited, despite Persian being one of the most widely spoken languages in West Asia, necessitating the development of NER methods for it. In this study, using Active Learning, a corpus of 1,351 advertisements from the Official Gazette was annotated. The GEMMA2b model was then fine-tuned on this data, achieving approximately 95% accuracy. This model was employed to extract around 13 types of named entities and their relationships within the advertisement texts. The primary advantage of this method is the model’s high accuracy compared to other approaches. Additionally, the use of Persian data—which, unlike languages such as English or Arabic, has fewer resources—is another notable feature of this research.