这篇论文展示了LLM在维和任务威胁评估中的实际应用，与人类判断高度一致，实用性强。
该论文提出将LLM应用于外国维和任务的威胁评估，基于PINPOINT项目和欧盟驻格鲁吉亚监测团的用例。工作流结合跨学科风险模型、OSINT媒体收集和LLM威胁提取，将媒体内容映射到任务相关威胁并提取结构化信息。评估显示自动结果与人类判断在威胁和任务相关性上高度一致。表明LLM可作为支持分析师的有效工具。
Application of LLMs to Threat Assessment of Foreign Peacekeeping Missions
We present a novel approach for applying Large Language Models (LLMs) to threat assessment in the context of foreign peacekeeping missions. Building on the PINPOINT project and its use case, the EU Monitoring Mission in Georgia, we combine an interdisciplinary risk-model with OSINT-based media collection and LLM-supported threat extraction. The proposed workflow maps media contents to mission-relevant threats, extracts structured information and applies several additional LLM-based processing steps to improve relevance and grounding. An evaluation of threats extracted from media documents shows high agreement between automatically generated results and human judgment for core aspects such as threat and mission relevance. These results indicate that LLMs provide a promising approach to support analysts in the context of peacekeeping missions.