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Extracting Dataset Mentions in Forced Displacement and FCV Documents: A Weakly Supervised Framework with LLM-Based Label Refinement — arXiv:2609.12107

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.12107
License (as archived): CC0-1.0
Archived: 2026-10-16T03:03:12.187Z
SHA-256 of the archived text: cc519149b8087d02b99d3ef5dc90d25a94553844f7fbb976a1470bf33dc8e596
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How to cite

Extracting Dataset Mentions in Forced Displacement and FCV Documents: A Weakly Supervised Framework with LLM-Based Label Refinement — arXiv:2609.12107 — arXiv (en), https://arxiv.org/abs/2609.12107 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f009d4e3513d (Metraipolis archive dc_f009d4e3513d, sha256 cc519149b8087d02b99d3ef5dc90d25a94553844f7fbb976a1470bf33dc8e596)

BibTeX

@misc{dcf009d4e3513d,
  title = {Extracting Dataset Mentions in Forced Displacement and FCV Documents: A Weakly Supervised Framework with LLM-Based Label Refinement — arXiv:2609.12107},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.12107},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f009d4e3513d; sha256 cc519149b8087d02b99d3ef5dc90d25a94553844f7fbb976a1470bf33dc8e596},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.12107. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 dispersed across research papers, project documents, humanitarian reports, and other unstructured text, limiting both the ability to trace data use and to identify potential gaps in data availability or dissemination. We present a weakly supervised framework for adapting dataset extraction to forced displacement and Fragile, Conflict, and Violence (FCV) documents without first constructing a large manually labeled training corpus. A lightweight model trained on general research literature generates candidate dataset mentions from unlabeled domain documents, which a frontier large language model (LLM) reviews in context, validating or rejecting candidates and correcting their extraction boundaries. The resulting annotations are supplemented with targeted synthetic and contrastive examples and used to fine-tune the lightweight model for large-scale extraction. We evaluate the resulting model on an independent gold-standard benchmark of 1,706 text passages spanning research, humanitarian, and operational documents. Across the full benchmark, the model achieves 74.1\% precision and 70.5\% recall at the mention level; among passages containing dataset references, precision reaches 89.5\%. At the passage level, the model achieves 88.2\% accuracy and 88.6\% specificity in distinguishing passages with dataset references from those without them. These results demonstrate a practical approach for constructing domain-specific supervision when labeled data are limited, and provide a technical foundation for larger-scale analysis of data use and potential gaps in the displacement data landscape.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-14 in cs.AI).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Econometrics (econ.EM).
Pagina del lavoro: https://arxiv.org/abs/2609.12107 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T02:51:27.860Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).