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ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
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Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, Matei Zaharia. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
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“This winner-take-all formulation has been shown to be both effective and computationally attractive in multi-vector retrieval systems [1, 4, 5].”
From Spike Hijacking in Late-Interaction Retrieval (2026) · cites this paper as [4]
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“We compare HippoRAG with existing RAG methods on multi-hop question answering (QA) and show that our method outperforms the state-of-the-art methods remarkably, by up to 20 %.”
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Metadata
- Authors
- Santhanam, K., Khattab, O., Saad-Falcon, J., Potts, C., & Zaharia, M.
- Affiliations
- Georgia Institute of Technology (US)
- Venue
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Published
- 2022
- Pages
- 3715-3734
- OpenAlex
- W3217305727
- Cited by
- 353
- References
- 74
- Citations by year
- 2026: 123 · 2025: 99 · 2024: 49 · 2023: 64 · 2022: 18
- FWCI
- 51.41
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Funders
- National Science Foundation (1651570, CAREER)
- Cisco Systems
- VMware
- Ant Financial Services Group
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- Colbert, efficient retrieval, dense retrieval, word embeddings
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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