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Towards Automated Situation Awareness: A RAG-Based Framework for Peacebuilding Reports

Poli A. Nemkova, Suleyman O. Polat, Rafid I. Jahan, Sagnik Ray Choudhury, Sun-joo Lee, Shouryadipta Sarkar, Mark V. Albert

TL;DR

The paper tackles the challenge of timely, grounded situation awareness in peacebuilding by introducing a multimodal dynamic Retrieval-Augmented Generation (RAG) framework that ingests real-time data from news, conflict-event databases, and economic indicators to generate structured, evidence-backed reports. It combines a knowledge-base on demand with a two-prompt LLM generation approach and a three-level evaluation pipeline (automated metrics, human expert review, and LLM-based judging) to ensure factuality and usability. Key contributions include the first peacebuilding application of RAG with LLM grounding, the ragve verifier for ground-truth alignment, and open-source release of code and evaluation tools. The approach promises faster decision-making, broader coverage, and transparent, reproducible reporting for NGOs and UN agencies, while acknowledging remaining challenges in bias, regional data gaps, and the need for human-in-the-loop oversight.

Abstract

Timely and accurate situation awareness is vital for decision-making in humanitarian response, conflict monitoring, and early warning and early action. However, the manual analysis of vast and heterogeneous data sources often results in delays, limiting the effectiveness of interventions. This paper introduces a dynamic Retrieval-Augmented Generation (RAG) system that autonomously generates situation awareness reports by integrating real-time data from diverse sources, including news articles, conflict event databases, and economic indicators. Our system constructs query-specific knowledge bases on demand, ensuring timely, relevant, and accurate insights. To ensure the quality of generated reports, we propose a three-level evaluation framework that combines semantic similarity metrics, factual consistency checks, and expert feedback. The first level employs automated NLP metrics to assess coherence and factual accuracy. The second level involves human expert evaluation to verify the relevance and completeness of the reports. The third level utilizes LLM-as-a-Judge, where large language models provide an additional layer of assessment to ensure robustness. The system is tested across multiple real-world scenarios, demonstrating its effectiveness in producing coherent, insightful, and actionable reports. By automating report generation, our approach reduces the burden on human analysts and accelerates decision-making processes. To promote reproducibility and further research, we openly share our code and evaluation tools with the community via GitHub.

Towards Automated Situation Awareness: A RAG-Based Framework for Peacebuilding Reports

TL;DR

The paper tackles the challenge of timely, grounded situation awareness in peacebuilding by introducing a multimodal dynamic Retrieval-Augmented Generation (RAG) framework that ingests real-time data from news, conflict-event databases, and economic indicators to generate structured, evidence-backed reports. It combines a knowledge-base on demand with a two-prompt LLM generation approach and a three-level evaluation pipeline (automated metrics, human expert review, and LLM-based judging) to ensure factuality and usability. Key contributions include the first peacebuilding application of RAG with LLM grounding, the ragve verifier for ground-truth alignment, and open-source release of code and evaluation tools. The approach promises faster decision-making, broader coverage, and transparent, reproducible reporting for NGOs and UN agencies, while acknowledging remaining challenges in bias, regional data gaps, and the need for human-in-the-loop oversight.

Abstract

Timely and accurate situation awareness is vital for decision-making in humanitarian response, conflict monitoring, and early warning and early action. However, the manual analysis of vast and heterogeneous data sources often results in delays, limiting the effectiveness of interventions. This paper introduces a dynamic Retrieval-Augmented Generation (RAG) system that autonomously generates situation awareness reports by integrating real-time data from diverse sources, including news articles, conflict event databases, and economic indicators. Our system constructs query-specific knowledge bases on demand, ensuring timely, relevant, and accurate insights. To ensure the quality of generated reports, we propose a three-level evaluation framework that combines semantic similarity metrics, factual consistency checks, and expert feedback. The first level employs automated NLP metrics to assess coherence and factual accuracy. The second level involves human expert evaluation to verify the relevance and completeness of the reports. The third level utilizes LLM-as-a-Judge, where large language models provide an additional layer of assessment to ensure robustness. The system is tested across multiple real-world scenarios, demonstrating its effectiveness in producing coherent, insightful, and actionable reports. By automating report generation, our approach reduces the burden on human analysts and accelerates decision-making processes. To promote reproducibility and further research, we openly share our code and evaluation tools with the community via GitHub.
Paper Structure (11 sections, 2 figures, 3 tables)

This paper contains 11 sections, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Method and System Design
  4. Evaluation
  5. Results and Discussion
  6. Human vs. LLM Evaluation
  7. Strengths and Limitations of the Approach
  8. Regional Variations in Model Performance
  9. Benefits for Real-World Applications
  10. Future Work
  11. Conclusion

Figures (2)

  • Figure 1: Overall system design presented as a flowchart. Created with Miro.
  • Figure 2: Report evaluation three-level framework. Created with Miro.