The DOI matches its Crossref record; 72 of the 74 references it cites were found in a registry or an index.
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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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208
Billion-scale similarity search with GPUs
Johnson, J., Douze, M., & Jegou, H. (2021) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2019.2921572 · Cited by 4,228
Dense passage retrieval for open-domain question answering
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.550 · Cited by 3,575
Product Quantization for Nearest Neighbor Search
Jégou, H., Douze, M., & Schmid, C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.57 · Cited by 2,956 · cited 1×
“Product quantization (Gray, 1984; Jegou et al., 2010) compresses a single vector by splitting it into small sub-vectors and encoding each of them using an ID within a codebook.”
BackgroundMS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Bajaj, P., Campos, D., Craswell, N., Deng, L., Gao, J., Liu, X., Majumder, R., McNamara, A., Mitra, B., Nguyen, T., et al. (2016) · arXiv · doi:10.48550/arxiv.1611.09268 · Cited by 2,473
DBpedia: A Nucleus for a Web of Open Data
Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., & Ives, Z. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-76298-0_52 · Cited by 2,454
Natural Questions: A benchmark for question answering research
Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00276 · Cited by 2,069
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1259 · Cited by 1,804
Research works printed but not linked: 7
- Alexander Bondarenko, Maik Fröbe, Meriem Be- loucif, Lukas Gienapp, Yamen Ajjour, Alexander Panchenko, Chris Biemann, Benno Stein, Henning Wachsmuth, Martin Potthast, et al. 2020. Overview of touché 2020: Argument Retrieval. In Interna- tional Conference of the Cross-Language Evalua- tion Forum for European Languages, pages 384- 395. Springer.
- Robert Gray. 1984. Vector quantization. IEEE Assp Magazine, 1(2):4-29.
- PMLR.
- Macedo Maia, Siegfried Handschuh, André Freitas, Brian Davis, Ross McDermott, Manel Zarrouk, and Alexandra Balahur. 2018. WWW'18 Open Chal- lenge: Financial Opinion Mining and Question An- swering. In Companion Proceedings of the The Web Conference 2018, pages 1941-1942.
- Nandan Thakur, Nils Reimers, Andreas Rücklé, Ab- hishek Srivastava, and Iryna Gurevych. 2021. BEIR: A Heterogenous Benchmark for Zero-shot Eval- uation of Information Retrieval Models. arXiv preprint arXiv:2104.08663. · arXiv
- Ikuya Yamada, Akari Asai, and Hannaneh Hajishirzi. 2021b. Efficient passage retrieval with hashing for open-domain question answering. In Proceedings of the 59th Annual Meeting of the Association for Com- putational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 979-986, Online. Association for Computational Linguistics. · DOI
- Peilin Yang, Hui Fang, and Jimmy Lin. 2018a. Anserini: Reproducible ranking baselines using lucene. Journal of Data and Information Quality (JDIQ), 10(4):1-20.
Cited by
Sorted by citations
Items 101–110 of 110
GLEN: Generative Retrieval via Lexical Index Learning
Lee, S., Choi, M., & Lee, J. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.477 · Cited by 10
Improved Learned Sparse Retrieval with Corpus-Specific Vocabularies
Yu, P., Mallia, A., & Petri, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56063-7_12 · Cited by 6
Clavié, B. (2025) · Journal of Natural Language Processing · doi:10.5715/jnlp.32.176 · Cited by 19
ColBERT Retrieval and Ensemble Response Scoring for Language Model Question Answering
Gichamba, A., Idris, T. K., Ebiyau, B., Nyberg, E., & Mitamura, T. (2024) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshp64532.2024.11100297 · Cited by 10
Sharma, P., & Bhattarai, S. (2026) · Journal of Future Artificial Intelligence and Technologies · doi:10.62411/faith.3048-3719-297 · Cited by 10
Pipeline and dataset generation for automated fact-checking in almost any language
Drchal, J., Ullrich, H., Mlynář, T., & Moravec, V. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10113-5 · Cited by 8
An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models
Liu, Q., Guo, G., Mao, J., Dou, Z., Wen, J.-R., Jiang, H., Zhang, X., & Cao, Z. (2024) · ACM Transactions on Information Systems · doi:10.1145/3639818 · Cited by 12
Li, Y., Fu, X., Verma, G., Buitelaar, P., & Liu, M. (2025) · arXiv · doi:10.48550/arxiv.2510.24476 · Cited by 4
BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents
Chen, Z., Ma, X., Zhuang, S., Nie, P., Zou, K., Sharifymoghaddam, S., Liu, A., Green, J., Patel, K., Meng, R., et al. (2026) · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2026.acl-long.1023 · Cited by 2
“Jina-ColBERT-v2 (Jha et al., 2024) is a late-interaction retriever that trains ColBERTv2 (Santhanam et al., 2022) from a newer BERT backbone.”
Spike Hijacking in Late-Interaction Retrieval
Suresh, K., Vatsa, T., King, T., Kadav, A., & Friedrich, M. (2026) · arXiv · doi:10.48550/arxiv.2604.05253
“This winner-take-all formulation has been shown to be both effective and computationally attractive in multi-vector retrieval systems [1, 4, 5].”
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
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