Extracting Dataset Mentions in Forced Displacement and FCV Documents: A Weakly Supervised Framework with LLM-Based Label Refinement
Automating dataset traceability in humanitarian research through advanced weakly supervised LLM frameworks.
Development and humanitarian organizations produce and support surveys, administrative registries, and other data resources to inform research, policy, and operations, yet systematically identifying where these datasets are referenced remains difficult. Such references are disper...
ArXiv CS.CL
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This research addresses the critical data discovery gap in humanitarian sectors by automating the identification of fragmented dataset citations. By leveraging weakly supervised learning and LLM-based refinement, the framework provides a scalable path toward evidence-based policy in fragile, conflict, and violence-affected contexts.
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