2025
COLING
COLING 2025
XTR meets ColBERTv2: Adding ColBERTv2 Optimizations to XTR
Abstract
AbstractXTR (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.
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Interdisciplinary Bridge
— Computer Science and Deep Learning and Machine Learning
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Keyword Pioneer
— vector indexing
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio