LLM pipeline extracts causal evidence from crisis reports with high accuracy

Researchers have developed a two-stage Large Language Model (LLM) pipeline to extract structured causal evidence from humanitarian crisis reports. This system, tested on ReliefWeb data from 2000-2024, aims to consolidate decision-relevant information that is often obscured by the length and multi-topic nature of these reports. The pipeline achieved high accuracy, with a fine-tuned Llama-3.1-8B model reaching a 94.15% weighted F1 score, outperforming a closed-source LLM. The study also introduced a triangulation method to aggregate evidence strength across different contexts, demonstrating strong convergence for cash assistance interventions. AI

IMPACT This research demonstrates a novel application of LLMs for structured data extraction from complex documents, potentially improving decision-making in crisis response.

RANK_REASON The cluster contains an academic paper detailing a new methodology and benchmark results for LLM application. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Hugging Face
• large language model
• Llama-3.1:8b
• ReliefWeb

• paper
• product
• infra

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