@inproceedings{nemkova-etal-2026-towards,
title = "Towards Automated Situation Awareness: A {RAG}-Based Framework for Peacebuilding Reports",
author = "Nemkova, Poli and
Polat, Suleyman Olcay and
Jahan, Rafid Ishrak and
Choudhury, Sagnik Ray and
Lee, Sun-Joo and
Sarkar, Shouryadipta and
Albert, Mark V.",
editor = "Atwell, Katherine and
Borah, Angana and
Dementieva, Daryna and
Kreiss, Elisa and
Kotonya, Neema and
Liu, Jiarui and
Olson, Liz and
Wan, Ruyuan and
Zhao, Jieyu",
booktitle = "Proceedings of the Fifth Workshop on {NLP} for Positive Impact ({NLP}4{PI})",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlp4pi-1.39/",
pages = "511--529",
ISBN = "979-8-89176-495-8",
abstract = "Timely and accurate situation awareness underpins effective decision-making in humanitarian response, conflict monitoring, and peacebuilding. Yet, synthesizing heterogeneous and rapidly evolving information from news, conflict databases, and economic indicators remains labor-intensive and delays critical interventions. We present a Retrieval-Augmented Generation (RAG) system that autonomously produces structured, evidence-backed situation awareness reports by integrating real-time data from GDELT, ACLED, ReliefWeb, and World Bank APIs. To rigorously assess report quality without ground-truth references, we introduce a three-level reference-free evaluation framework combining automated NLP metrics, expert review by United Nations crisis analysts, and scalable LLM-as-a-Judge assessment. In a multi-country study across 15 conflict-prone regions, our system generated coherent, relevant, and actionable reports, with UNDP analysts estimating a {\textasciitilde}50{\%} reduction in report preparation time. These findings demonstrate the feasibility of deploying RAG-based systems for peacebuilding and humanitarian operations and provide a reproducible framework for generating and evaluating AI-assisted situational intelligence at scale."
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<abstract>Timely and accurate situation awareness underpins effective decision-making in humanitarian response, conflict monitoring, and peacebuilding. Yet, synthesizing heterogeneous and rapidly evolving information from news, conflict databases, and economic indicators remains labor-intensive and delays critical interventions. We present a Retrieval-Augmented Generation (RAG) system that autonomously produces structured, evidence-backed situation awareness reports by integrating real-time data from GDELT, ACLED, ReliefWeb, and World Bank APIs. To rigorously assess report quality without ground-truth references, we introduce a three-level reference-free evaluation framework combining automated NLP metrics, expert review by United Nations crisis analysts, and scalable LLM-as-a-Judge assessment. In a multi-country study across 15 conflict-prone regions, our system generated coherent, relevant, and actionable reports, with UNDP analysts estimating a ~50% reduction in report preparation time. These findings demonstrate the feasibility of deploying RAG-based systems for peacebuilding and humanitarian operations and provide a reproducible framework for generating and evaluating AI-assisted situational intelligence at scale.</abstract>
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%0 Conference Proceedings
%T Towards Automated Situation Awareness: A RAG-Based Framework for Peacebuilding Reports
%A Nemkova, Poli
%A Polat, Suleyman Olcay
%A Jahan, Rafid Ishrak
%A Choudhury, Sagnik Ray
%A Lee, Sun-Joo
%A Sarkar, Shouryadipta
%A Albert, Mark V.
%Y Atwell, Katherine
%Y Borah, Angana
%Y Dementieva, Daryna
%Y Kreiss, Elisa
%Y Kotonya, Neema
%Y Liu, Jiarui
%Y Olson, Liz
%Y Wan, Ruyuan
%Y Zhao, Jieyu
%S Proceedings of the Fifth Workshop on NLP for Positive Impact (NLP4PI)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-495-8
%F nemkova-etal-2026-towards
%X Timely and accurate situation awareness underpins effective decision-making in humanitarian response, conflict monitoring, and peacebuilding. Yet, synthesizing heterogeneous and rapidly evolving information from news, conflict databases, and economic indicators remains labor-intensive and delays critical interventions. We present a Retrieval-Augmented Generation (RAG) system that autonomously produces structured, evidence-backed situation awareness reports by integrating real-time data from GDELT, ACLED, ReliefWeb, and World Bank APIs. To rigorously assess report quality without ground-truth references, we introduce a three-level reference-free evaluation framework combining automated NLP metrics, expert review by United Nations crisis analysts, and scalable LLM-as-a-Judge assessment. In a multi-country study across 15 conflict-prone regions, our system generated coherent, relevant, and actionable reports, with UNDP analysts estimating a ~50% reduction in report preparation time. These findings demonstrate the feasibility of deploying RAG-based systems for peacebuilding and humanitarian operations and provide a reproducible framework for generating and evaluating AI-assisted situational intelligence at scale.
%U https://aclanthology.org/2026.nlp4pi-1.39/
%P 511-529
Markdown (Informal)
[Towards Automated Situation Awareness: A RAG-Based Framework for Peacebuilding Reports](https://aclanthology.org/2026.nlp4pi-1.39/) (Nemkova et al., NLP4PI 2026)
ACL