COLING 2025industry0 citations
XTR meets ColBERTv2: Adding ColBERTv2 Optimizations to XTR
Abstract
XTR (Lee et al., 2023) introduced an efficient multi-vector retrieval method that addresses the limitations of the ColBERT (Khattab and Zaharia, 2020model by simplifying retrieval into a single stage through a modified learning objective. While XTR eliminates the need for multistage retrieval, it doesn’t incorporate the efficiency optimizations from ColBERTv2 (Santhanam et al., 2022, which improve indexing and retrieval speed. In this work, we enhance XTR by integrating ColBERTv2’s optimizations, showing that the combined approach preserves the strengths of both models. This results in a more efficient and scalable solution for multi-vector retrieval, while maintaining XTR’s streamlined retrieval process.
BibTeX
@inproceedings{bhat-sen-2025-xtr,
title = "{XTR} meets {C}ol{BERT}v2: Adding {C}ol{BERT}v2 Optimizations to {XTR}",
author = "Bhat, Riyaz Ahmad and
Sen, Jaydeep",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven and
Darwish, Kareem and
Agarwal, Apoorv",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics: Industry Track",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-industry.30/",
pages = "358--365"
}