用大模型分析媒体信息，自动识别外派维和任务中的威胁。
Application of LLMs to Threat Assessment of Foreign Peacekeeping Missions
- 结合风险模型与开源情报，用大模型提取威胁信息。
- 自动提取结果与人工判断在核心要素上高度一致。
- 适合维和任务分析员快速获取关键威胁信息。
我们提出一种将大型语言模型（LLMs）应用于外国维和任务威胁评估的新方法。基于PINPOINT项目及其在欧盟格鲁吉亚监测任务中的应用场景，该方法融合跨学科风险模型、基于开源情报（OSINT）的媒体数据收集以及大模型支持的威胁提取。提出的流程将媒体内容映射至任务相关威胁，提取结构化信息，并通过多个额外的基于LLM的处理步骤提升信息的相关性和可信度。对媒体文档中提取的威胁进行评估显示，自动结果与人工判断在威胁性质及任务相关性等核心方面具有高度一致性。结果表明，大模型为维和任务分析提供了有前景的支持方案。
原文摘要 · Abstract (English)
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.
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