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OA

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.