A large-language-model framework for automated humanitarian situation reporting

Timely and accurate situational reports are essential for humanitarian decision-making, yet current workflows remain largely manual, resource intensive, and inconsistent. We present a fully automated framework that uses large language models (LLMs) to transform heterogeneous humanitarian documents into structured and evidence-grounded reports. The system integrates semantic text clustering, automatic question generation, retrieval augmented answer extraction with citations, multi-level summarization, and executive summary generation, supported by internal evaluation metrics that emulate expert reasoning. We evaluated the framework across 13 humanitarian events, including natural disasters and conflicts, using more than 1100 documents from verified sources such as ReliefWeb. The generated questions achieved 84.7% relevance, 84.0% importance, and 76.4% urgency. The extracted answers reached 86.3% relevance, with citation precision and recall both exceeding 76%. Agreement between human and LLM based evaluations surpassed an F1 score of 0.80. Comparative analysis shows that the proposed framework produces reports that are more structured, interpretable, and actionable than existing baselines. By combining LLM reasoning with transparent citation linking and multi-level evaluation, this study shows that generative AI can substantially support the generation of humanitarian situation reports that domain experts judged more useful, better structured, and more transparently sourced than current approaches—marking a concrete step toward operational use, even as important challenges remain on the path to fully reliable autonomous deployment.