Enhancing trust in Large Language Models for streamlined decision-making in military operations

Abstract

Large Language Models (LLMs) have the potential to enhance decision-making significantly in core military operational contexts that support training, readiness, and mission execution under low-risk conditions. Still, their implementation must be approached carefully, considering the associated risks. This paper examines the integration of LLMs into military decision-making, emphasizing the LLM’s ability to improve intelligence analysis, enhance situational awareness, support strategic planning, predict threats, optimize logistics, and strengthen cybersecurity. The paper also considers misinterpretation, bias, misinformation, or overreliance on AI-generated suggestions, potentially leading to errors in routine but critical decision-making processes. Our work concludes by proposing solutions and promoting the responsible implementation of LLMs to ensure their effective and ethical use in military operations. To build trust in LLMs, this paper advocates for developing cybersecurity frameworks, transparency, and ethical oversight. It further suggests using machine unlearning (MU) to selectively remove outdated or compromised data from LLM training datasets, preserving the integrity of the insights they generate. The paper underscores the imperative for integrating LLMs in low-risk military contexts, coupled with sustained research efforts to mitigate potential hazards.