Researchers have developed a two-stage Large Language Model (LLM) pipeline to extract structured causal evidence from lengthy and complex humanitarian reports. This system, tested on ReliefWeb data from 2000-2024, uses query-conditioned extraction and snippet grounding for auditability. In expert-annotated tests, a fine-tuned Llama-3.1-8B model achieved a 94.15% F1 score, outperforming a closed-source LLM. The pipeline also incorporates a triangulation method to quantify evidence convergence across different contexts, demonstrating strong positive results for cash assistance and food-related outcomes. AI
IMPACT This research demonstrates a significant advancement in using LLMs for structured data extraction from complex documents, potentially improving evidence synthesis in critical fields like humanitarian aid.
RANK_REASON The cluster describes a research paper detailing a new methodology and model performance on a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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