The overwhelming success of GPT-4 in early 2023 highlighted the transformative potential of large language models (LLMs) across various sectors, including national security. This article explores the implications of LLM integration within national security contexts, analyzing their potential to revolutionize information processing, decision-making, and operational efficiency. Whereas LLMs offer substantial benefits, such as automating tasks and enhancing data analysis, they also pose significant risks, including hallucinations, data privacy concerns, and vulnerability to adversarial attacks. Through their coupling with decision-theoretic principles and Bayesian reasoning, LLMs can significantly improve decision-making processes within national security organizations. Namely, LLMs can facilitate the transition from data to actionable decisions, enabling decision-makers to quickly receive and distill available information with less manpower. Current applications within the US Department of Defense and beyond are explored, e.g., the USAF's use of LLMs for wargaming and automatic summarization, that illustrate their potential to streamline operations and support decision-making. However, these applications necessitate rigorous safeguards to ensure accuracy and reliability. The broader implications of LLM integration extend to strategic planning, international relations, and the broader geopolitical landscape, with adversarial nations leveraging LLMs for disinformation and cyber operations, emphasizing the need for robust countermeasures. Despite exhibiting "sparks" of artificial general intelligence, LLMs are best suited for supporting roles rather than leading strategic decisions. Their use in training and wargaming can provide valuable insights and personalized learning experiences for military personnel, thereby improving operational readiness.

翻译：2023年初GPT-4的巨大成功凸显了大型语言模型（LLMs）在包括国家安全在内的各领域的变革潜力。本文探讨了LLMs在国家安全背景下的整合影响，分析了其在信息处理、决策制定和作战效能方面可能带来的革命性变化。尽管LLMs能带来显著效益——如任务自动化与数据分析增强——它们也伴随着重大风险，包括幻觉生成、数据隐私问题及对抗性攻击的脆弱性。通过与决策理论原则及贝叶斯推理的结合，LLMs能显著提升国家安全机构内的决策流程效率。具体而言，LLMs能够促进从数据到可执行决策的转化，使决策者能以更少人力快速接收并提炼可用信息。本文考察了美国国防部等机构的现有应用案例（例如美国空军将LLMs用于兵棋推演与自动摘要），这些案例展示了LLMs在简化作战流程与辅助决策方面的潜力。然而，此类应用需要严格的安全保障机制以确保准确性与可靠性。LLMs整合的广泛影响延伸至战略规划、国际关系及地缘政治格局——对手国家可能利用LLMs开展虚假信息宣传与网络作战，这凸显了建立强韧应对机制的必要性。尽管展现出通用人工智能的"星火"特征，LLMs目前最适合承担辅助角色而非主导战略决策。其在军事训练与兵棋推演中的应用能为军事人员提供有价值的洞察和个性化学习体验，从而提升战备水平。