The Research on Military-RAG Question Answering System Based on MiliGNER

The Research on Military-RAG Question Answering System Based on MiliGNER | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 29 December 2025 V1 Latest version Share on The Research on Military-RAG Question Answering System Based on MiliGNER Authors : ZhangJiaqi and ZhiRuicong [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176701189.97854602/v1 123 views 67 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract With the advancement of large language models, generative named entity recognition (Generative NER) has emerged as a key technology for intelligent question-answering systems in specialized domains. This paper proposes the Military-RAG system based on MiliGNER (Military Generative NER) to directly generate structured triples (e.g., [“J-20 fighter jet”, “maximum speed”, “2.0 Mach”]), effectively enhancing the accuracy and flexibility of military entity recognition. The system incorporates multi-path output and error correction mechanisms during generation, combined with an entity attribute validation module, to ensure the stability and reasonableness of recognition results. Leveraging the RAG framework, the system integrates high-precision structured entity recognition with large language model generation, significantly improving the accuracy, semantic consistency, and robustness of question-answering results. Experimental results demonstrate that this approach outperforms traditional baseline models across multiple core metrics, validating its application value and technical potential in military question-answering tasks. Supplementary Material File (paper-en.docx) Download 1.31 MB Information & Authors Information Version history V1 Version 1 29 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords error correction mechanism generative named entity recognition question-answering systems structured information extraction Authors Affiliations ZhangJiaqi University of Science and Technology Beijing View all articles by this author ZhiRuicong [email protected] University of Science and Technology Beijing View all articles by this author Metrics & Citations Metrics Article Usage 123 views 67 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation ZhangJiaqi, ZhiRuicong. The Research on Military-RAG Question Answering System Based on MiliGNER. Authorea . 29 December 2025. DOI: https://doi.org/10.22541/au.176701189.97854602/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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