Frontiers of Data and Computing ›› 2025, Vol. 7 ›› Issue (5): 184-197.

CSTR: 32002.14.jfdc.CN10-1649/TP.2025.05.015

doi: 10.11871/jfdc.issn.2096-742X.2025.05.015

• Technology and Application • Previous Articles     Next Articles

Method On Knowledge Extraction of Radar Domains Based on Prompt Learning

HUANG Zhenming(),WU Xiaofang*(),XUE Mengwu   

  1. 32802 of the People’s Liberation Army Unit, Beijing 100191, China
  • Received:2025-01-17 Online:2025-10-20 Published:2025-10-23
  • Contact: WU Xiaofang E-mail:1213815602@qq.com;542771912@qq.com

Abstract:

[Objective] Dual information in the field of radar provides a clearer and more complete description of the performance of radar equipment, but fewer studies have been conducted on such problems. In this paper, a radar knowledge extraction method based on Prompt Learning and LoRA fine-tuning LLM (RKEPLL) is proposed for knowledge extraction in the field of radar equipment. [Method] A prompt learning template and keyword combination in the field of radar equipment are designed to model dual information extraction into information combination generation. In addition, LoRA fine-tuning is performed on LLaMA3, and the method is also validated by constructing a domain dataset using data from the WeChat Official Accounts and radar manuals. [Conclusion] Efficient results have been achieved on the self-constructed dataset, whose BLEU-4, ROUGE-1, ROUGE-2, and ROUGE-L are 97.1406%, 99.2544%, 99.0758% and 97.9330%, respectively, and have certain practical application ability, which provides an efficient method for solving the dual information extraction in the field of radar equipment.

Key words: prompt learning, information combination generation, dual information extraction, radar equipment