电讯技术2026，Vol.66Issue(8)：1253-1260,8.DOI:10.20079/j.issn.1001-893x.251213001
融合对抗性推理与多维自适应融合的大模型作战知识评估
Assessing LLM-generated Combat Knowledge via Adversarial Reasoning and Multidimensional Adaptive Fusion
摘要
Abstract
To address the challenges of"hallucinations"and multi-source intelligence conflicts inherent in Large Language Models(LLMs)when generating combat knowledge,a credibility assessment method that integrates adversarial evidence reasoning and adaptive feature fusion is proposed.First,to address the low intelligence recall rate under a single query model,a multi-view generation enhancement strategy is proposed,and a two-factor retrieval mechanism integrating semantic alignment and source authority is constructed.Second,an adversarial reasoning framework based on Chain-of-Thought(CoT)is designed to simulate the perspective of the"red team"and actively mine rebuttal evidence to suppress confirmation bias in the model.Finally,a supervised decision fusion model based on Multilayer Perceptron(MLP)is constructed to achieve a nonlinear mapping from multidimensional cognitive features to confidence scores.Experimental results on the constructed Combat Knowledge Hallucination Evaluation Dataset(CKHE)show that the proposed method achieves a Macro-F1 score of 85.41%,maintaining performance above 75%even under 80%high noise interference.关键词
作战知识评估/大语言模型(LLM)/对抗性证据推理/自适应特征融合Key words
combat knowledge assessment/large language model(LLM)/adversarial evidence reasoning/adaptive feature fusion分类
信息技术与安全科学引用本文复制引用
王伟..融合对抗性推理与多维自适应融合的大模型作战知识评估[J].电讯技术,2026,66(8):1253-1260,8.
