SALUTE：面向国防领域的大语言模型基准测试与适配
SALUTE: Benchmarking and Adapting LLMs for the Defense Domain
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中文总结 AI 辅助
SALUTE提出一个端到端框架，整合语料、指令、偏好及基准数据，通过多阶段后训练适配大语言模型至国防领域，在保持通用能力的同时显著提升国防任务性能。
中文摘要 AI 辅助
国防是一个知识密集型领域，需要精确理解专业术语、条令概念、作战程序以及不断演变的军事事件。尽管近期已有研究探索了语言技术在军事应用中的潜力，但现有工作仍然零散：它们往往针对特定任务，依赖有限的适配流程，或缺乏全面的国防领域评估。在本文中，我们提出了SALUTE，一个用于国防领域大语言模型基准测试与适配的端到端框架。SALUTE整合了Salute-Corpus（一个从美国公开军事条令和政府文件中精选的语料库）、Salute-Conv（一个基于条令来源和长达十年的国防新闻构建的接地指令数据集）、Salute-Pref（一个国防感知的偏好数据集）以及Salute-Bench（一个经过严格筛选的基准，用于评估模型对条令和国防新闻的理解与推理能力）。基于这些资源，我们通过多阶段后训练（包括持续预训练、监督微调和偏好对齐）训练了Salute-LLM。大量实验表明，Salute-LLM在国防领域取得了强劲性能，同时保持了具有竞争力的通用能力，证明了SALUTE作为国防领域大语言模型适配的端到端框架的有效性。
英文摘要
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.
发表机构
- Korea University（高丽大学）
- Sungkyunkwan University（成均馆大学）
- Hanwha Systems（韩华系统）
机构由 AI 辅助整理，请以论文原文为准。
