@inproceedings{bian-etal-2026-efficiently-explore,
    title = "How to Efficiently Explore Noisy Historical Data? Leveraging Corpus Pre-Targeting to Enhance Graph-based {RAG}",
    author = "Bian, Donghan  and
      Puren, Marie  and
      Cafiero, Florian",
    editor = "Alves, Diego  and
      Bizzoni, Yuri  and
      Degaetano-Ortlieb, Stefania  and
      Kazantseva, Anna  and
      Pagel, Janis  and
      Szpakowicz, Stan",
    booktitle = "Proceedings of the 10th Joint {SIGHUM} Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.latechclfl-1.23/",
    pages = "241--250",
    ISBN = "979-8-89176-373-9",
    abstract = "Graph-based Retrieval-Augmented Generation (RAG) is increasingly used to explore long, heterogeneous, and weakly structured corpora, including historical archives. However, in such settings, naive full-corpus indexing is often computationally costly and sensitive to OCR noise, document redundancy, and topical dispersion. In this paper, we investigate corpus pre-targeting strategies as an intermediate layer to improve the efficiency and effectiveness of graph-based RAG for historical research.We evaluate a set of pre-targeting heuristics tailored to single-hop and multi-hop of historical questions on HistoriQA-ThirdRepublic, a French question-answering dataset derived from parliamentary debates and contemporary newspapers. Our results show that appropriate pre-targeting strategies can improve retrieval recall by 3{--}5{\%} while reducing token consumption by 32{--}37{\%} compared to full-corpus indexing, without degrading coverage of relevant documents.Beyond performance gains, this work highlights the importance of corpus-level optimization for applying RAG to large-scale historical collections, and provides practical insights for adapting graph-based RAG pipelines to the specific constraints of digitized archives."
}