@inproceedings{al-qurishi-etal-2025-hildoc,
    title = "Hildoc: Leveraging {H}ilbert Curve Representation for Accurate and Efficient Document Retrieval",
    author = "AL-Qurishi, Muhammad  and
      Qian, Zhaozhi  and
      AL-Tam, Faroq  and
      Souissi, Riad",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.101/",
    pages = "1863--1876",
    ISBN = "979-8-89176-298-5",
    abstract = "Document retrieval is a critical challenge in information retrieval systems, where the goal is to efficiently retrieve relevant documents in response to a given query. Dense retrieval methods, which utilize vector embeddings to represent semantic information, require effective indexing to ensure fast and accurate retrieval. Existing methods, such as MEVI, have attempted to address this by using hierarchical K-Means for clustering, but they often face limitations in computational efficiency and retrieval accuracy. In this paper, we introduce the Hildoc Index, a novel document indexing approach that leverages the Hilbert Curve to map document embeddings onto a one-dimensional space. This innovative representation facilitates efficient clustering using a 1D quantile-based algorithm, ensuring uniform partition sizes and preserving the inherent structure of the data. As a result, Hildoc Index not only reduces training complexity but also enhances retrieval accuracy and speed during inference. Our method can be seamlessly integrated into both dense retrieval systems and hybrid ensemble systems. Through comprehensive experiments on standard benchmarks like MSMARCO Passage and Natural Questions, we demonstrate that the Hildoc Index significantly outperforms the current state-of-the-art MEVI in terms of both retrieval speed and recall. These results underscore the Hildoc Index as a solution for fast and accurate dense document retrieval."
}