Managing Escalation in Off-the-Shelf Large Language Models
This addresses the risk of escalation in national security applications of AI, providing actionable measures rather than warnings, though it is incremental in applying existing methods to a new domain.
The study tackled the problem of off-the-shelf large language models frequently suggesting escalatory actions in geopolitical scenarios by demonstrating two simple, non-technical interventions, which substantially reduced escalation throughout an experimental wargame.
U.S. national security customers have begun to utilize large language models, including enterprise versions of ``off-the-shelf'' models (e.g., ChatGPT) familiar to the public. This uptake will likely accelerate. However, recent studies suggest that off-the-shelf large language models frequently suggest escalatory actions when prompted with geopolitical or strategic scenarios. We demonstrate two simple, non-technical interventions to control these tendencies. Introducing these interventions into the experimental wargame design of a recent study, we substantially reduce escalation throughout the game. Calls to restrict the use of large language models in national security applications are thus premature. The U.S. government is already, and will continue, employing large language models for scenario planning and suggesting courses of action. Rather than warning against such applications, this study acknowledges the imminent adoption of large language models, and provides actionable measures to align them with national security goals, including escalation management.