@inproceedings{zhang-etal-2025-strategies,
    title = "Strategies for Efficient Retrieval-augmented Generation in Clinical Domains with {RAPTOR}: A Benchmarking Study",
    author = "Zhang, Xumou  and
      Hu, Qixuan  and
      Kim, Jinman  and
      Dunn, Adam G.",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.164/",
    pages = "1420--1429",
    abstract = "The Recursive Abstractive Processing for Tree-Organized Retrieval (RAPTOR) framework deploys a hierarchical tree-structured datastore to integrate local and global context, enabling efficient handling of long documents for language models. This design is especially useful when cloud-based language models are unavailable or undesirable. For instance, with offline confidential patient records or stringent data-privacy requirements. We benchmarked RAPTOR on the QuALITY dataset and a novel Clinical Trial question-answering dataset (CTQA) drawn from over 500 000 registry entries. Experiments varied question complexity (simple vs. complex), four language models, four embedding models, and three chunking strategies. Also incorporated GPT-4o as a cloud-based baseline. Results show that, with optimal settings, RAPTOR combined with smaller local models outperforms GPT-4o on complex CTQA questions, although this gain does not extend to QuALITY. These outcomes highlight RAPTOR{'}s promise as a practical, locally implementable solution for long-context understanding."
}