SALUTE: Benchmarking and Adapting LLMs for the Defense Domain

2026-09-14

AI Summary

本文提出SALUTE框架，通过整合专门语料库和多阶段训练方法，解决国防领域语言模型适应性问题，提高其在该领域的性能。

Abstract

Defense is a knowledge-intensive domain that requires precise understanding of specialized terminology, doctrinal concepts, operational procedures, and evolving military events. Although recent work has explored language technologies for military applications, existing efforts remain fragmented: they are often task-specific, rely on limited adaptation pipelines, or lack comprehensive defense-domain evaluation. In this paper, we present SALUTE, an end-to-end framework for benchmarking and adapting LLMs for the defense domain. SALUTE integrates Salute-Corpus, a curated corpus from open-access U.S. military doctrine and government documents; Salute-Conv, a grounded instruction dataset from doctrinal sources and decade-long defense news; Salute-Pref, a defense-aware preference dataset; and Salute-Bench, a rigorously filtered benchmark for evaluating defense-domain understanding and reasoning over doctrine and defense news. Based on these resources, we train Salute-LLM through multi-stage post-training with continual pretraining, supervised fine-tuning, and preference alignment. Extensive experiments show that Salute-LLM achieves strong defense-domain performance while retaining competitive general capabilities, demonstrating the effectiveness of SALUTE as an end-to-end framework for defense-domain LLM adaptation.

Problem

• defense domain
• language models
• benchmarking
• adaptation
• military applications

Innovation

• end-to-end framework
• defense-domain adaptation
• multi-stage post-training
• SALUTE
• Salute-LLM