ESPN: Memory-Efficient Multi-vector Information Retrieval
Pages 95 - 107
Abstract
Recent advances in large language models have demonstrated remarkable effectiveness in information retrieval (IR) tasks. While many neural IR systems encode queries and documents into single-vector representations, multi-vector models elevate the retrieval quality by producing multi-vector representations and facilitating similarity searches at the granularity of individual tokens. However, these models significantly amplify memory requirements for retrieval indices by an order of magnitude. This escalation in index size renders the scalability of multi-vector IR models progressively challenging due to their substantial memory demands. We introduce Embedding from Storage Pipelined Network (ESPN) where we offload the entire re-ranking embedding tables to SSDs and reduce the memory requirements by 5−16×. We design a flexible software prefetcher applicable to any hierarchical clustering based search, achieving hit rates exceeding 90%. ESPN improves SSD based retrieval up to 6.4× and end-to-end throughput by 68% to maintain near-memory levels of query latency even for large query batch sizes. The code is available at https://github.com/susavlsh10/ESPN-v1.
References
[1]
Jonghyun Bae, Jongsung Lee, Yunho Jin, Sam Son, Shine Kim, Hakbeom Jang, Tae Jun Ham, and Jae W Lee. 2021. $FlashNeuron$:$SSD-Enabled$$Large-Batch$ Training of Very Deep Neural Networks. In 19th USENIX Conference on File and Storage Technologies (FAST 21). 387–401.
[2]
Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, and Tong Wang. 2018. MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. arxiv:1611.09268.
[3]
Dmitry Baranchuk, Artem Babenko, and Yury Malkov. 2018. Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors. In Computer Vision – ECCV 2018, Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and Yair Weiss (Eds.). Springer International Publishing, Cham. 209–224. isbn:978-3-030-01258-8
[4]
Qi Chen, Bing Zhao, Haidong Wang, Mingqin Li, Chuanjie Liu, Zengzhong Li, Mao Yang, and Jingdong Wang. 2021. SPANN: Highly-efficient Billion-scale Approximate Nearest Neighbor Search. In 35th Conference on Neural Information Processing Systems (NeurIPS 2021).
[5]
David R. Cheriton. 2019. From doc2query to docTTTTTquery. https://api.semanticscholar.org/CorpusID:208612557
[6]
Aditya Chilukuri and Shoaib Akram. 2023. Analyzing and Improving the Scalability of In-Memory Indices for Managed Search Engines. In Proceedings of the 2023 ACM SIGPLAN International Symposium on Memory Management (ISMM 2023). Association for Computing Machinery, New York, NY, USA. 15–29. isbn:9798400701795 https://doi.org/10.1145/3591195.3595272
[7]
Nachshon Cohen, Amit Portnoy, Besnik Fetahu, and Amir Ingber. 2022. SDR: Efficient Neural Re-ranking using Succinct Document Representation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Dublin, Ireland. 6624–6637. https://doi.org/10.18653/v1/2022.acl-long.457
[8]
Andrew Crotty, Viktor Leis, and Andrew Pavlo. 2022. Are You Sure You Want to Use MMAP in Your Database Management System? In CIDR 2022, Conference on Innovative Data Systems Research.
[9]
Zhuyun Dai and Jamie Callan. 2019. Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval. arxiv:1910.10687.
[10]
Bita Darvish Rouhani, Ritchie Zhao, Venmugil Elango, Rasoul Shafipour, Mathew Hall, Maral Mesmakhosroshahi, Ankit More, Levi Melnick, Maximilian Golub, Girish Varatkar, Lai Shao, Gaurav Kolhe, Dimitry Melts, Jasmine Klar, Renee L’Heureux, Matt Perry, Doug Burger, Eric Chung, Zhaoxia (Summer) Deng, Sam Naghshineh, Jongsoo Park, and Maxim Naumov. 2023. With Shared Microexponents, A Little Shifting Goes a Long Way. In Proceedings of the 50th Annual International Symposium on Computer Architecture (ISCA ’23). Association for Computing Machinery, New York, NY, USA. Article 83, 13 pages. isbn:9798400700958 https://doi.org/10.1145/3579371.3589351
[11]
Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni. 2004. Locality-sensitive hashing scheme based on p-stable distributions. In Proceedings of the Twentieth Annual Symposium on Computational Geometry (SCG ’04). Association for Computing Machinery, New York, NY, USA. 253–262. isbn:1581138857 https://doi.org/10.1145/997817.997857
[12]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In 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). Association for Computational Linguistics, Minneapolis, Minnesota. 4171–4186. https://doi.org/10.18653/v1/N19-1423
[13]
Thibault Formal, C. Lassance, Benjamin Piwowarski, and Stéphane Clinchant. 2021. SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval. ArXiv, abs/2109.10086 (2021), https://api.semanticscholar.org/CorpusID:237581550
[14]
Cong Fu, Chao Xiang, Changxu Wang, and Deng Cai. 2019. Fast approximate nearest neighbor search with the navigating spreading-out graph. Proc. VLDB Endow., 12, 5 (2019), jan, 461–474. issn:2150-8097 https://doi.org/10.14778/3303753.3303754
[15]
Luyu Gao and Jamie Callan. 2022. Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, Dublin, Ireland. 2843–2853. https://doi.org/10.18653/v1/2022.acl-long.203
[16]
Luyu Gao, Zhuyun Dai, and Jamie Callan. 2021. COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online. 3030–3042. https://doi.org/10.18653/v1/2021.naacl-main.241
[17]
2023. NVIDIA GPUDirect Storage Benchmarking and Configuration Guide. https://docs.nvidia.com/gpudirect-storage/configuration-guide/index.html
[18]
Ruiqi Guo, Philip Sun, Erik Lindgren, Quan Geng, David Simcha, Felix Chern, and Sanjiv Kumar. 2020. Accelerating Large-Scale Inference with Anisotropic Vector Quantization. In Proceedings of the 37th International Conference on Machine Learning, Hal Daumé III and Aarti Singh (Eds.) (Proceedings of Machine Learning Research, Vol. 119). PMLR, 3887–3896. https://proceedings.mlr.press/v119/guo20h.html
[19]
Kiana Hajebi, Yasin Abbasi-Yadkori, Hossein Shahbazi, and Hong Zhang. 2011. Fast approximate nearest-neighbor search with k-nearest neighbor graph. In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence - Volume Volume Two (IJCAI’11). AAAI Press, 1312–1317. isbn:9781577355144
[20]
Sebastian Hofstätter, Omar Khattab, Sophia Althammer, Mete Sertkan, and Allan Hanbury. 2022. Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions Using Enhanced Reduction. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM ’22). Association for Computing Machinery, New York, NY, USA. 737–747. isbn:9781450392365 https://doi.org/10.1145/3511808.3557367
[21]
Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Sebastian Riedel, and Edouard Grave. 2020. A Memory Efficient Baseline for Open Domain Question Answering. CoRR, abs/2012.15156 (2020), arXiv:2012.15156. arxiv:2012.15156
[22]
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2017. Billion-scale similarity search with GPUs. CoRR, abs/1702.08734 (2017), arXiv:1702.08734. arxiv:1702.08734
[23]
Herve Jégou, Matthijs Douze, and Cordelia Schmid. 2011. Product Quantization for Nearest Neighbor Search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 1 (2011), 117–128. https://doi.org/10.1109/TPAMI.2010.57
[24]
Hervé Jégou, Romain Tavenard, Matthijs Douze, and Laurent Amsaleg. 2011. Searching in one billion vectors: Re-rank with source coding. Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on, 02, https://doi.org/10.1109/ICASSP.2011.5946540
[25]
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense Passage Retrieval for Open-Domain Question Answering. CoRR, abs/2004.04906 (2020), arXiv:2004.04906. arxiv:2004.04906
[26]
M. J. Kerrisk. 2021. cgroups.7 - Linux manual page. https://man7.org/linux/man-pages/man7/cgroups.7.html
[27]
Omar Khattab and Matei Zaharia. 2020. ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’20). Association for Computing Machinery, New York, NY, USA. 39–48. isbn:9781450380164 https://doi.org/10.1145/3397271.3401075
[28]
Jinchun Kim, Seth H. Pugsley, Paul V. Gratz, A.L. Narasimha Reddy, Chris Wilkerson, and Zeshan Chishti. 2016. Path confidence based lookahead prefetching. In 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). 1–12. https://doi.org/10.1109/MICRO.2016.7783763
[29]
Brian Kulis and Trevor Darrell. 2009. Learning to Hash with Binary Reconstructive Embeddings. In Advances in Neural Information Processing Systems, Y. Bengio, D. Schuurmans, J. Lafferty, C. Williams, and A. Culotta (Eds.). 22, Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2009/file/6602294be910b1e3c4571bd98c4d5484-Paper.pdf
[30]
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020. Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. In Proceedings of the 34th International Conference on Neural Information Processing Systems (NIPS’20). Curran Associates Inc., Red Hook, NY, USA. Article 793, 16 pages. isbn:9781713829546
[31]
Minghan Li, Sheng-Chieh Lin, Barlas Oguz, Asish Ghoshal, Jimmy Lin, Yashar Mehdad, Wen tau Yih, and Xilun Chen. 2022. CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval. arxiv:2211.10411.
[32]
Jimmy Lin and Xueguang Ma. 2021. A Few Brief Notes on DeepImpact, COIL, and a Conceptual Framework for Information Retrieval Techniques. arxiv:2106.14807.
[33]
Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021. Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations. In Proceedings of the 44th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021). 2356–2362.
[34]
Ting Liu, Andrew Moore, Ke Yang, and Alexander Gray. 2004. An Investigation of Practical Approximate Nearest Neighbor Algorithms. In Advances in Neural Information Processing Systems, L. Saul, Y. Weiss, and L. Bottou (Eds.). 17, MIT Press. https://proceedings.neurips.cc/paper_files/paper/2004/file/1102a326d5f7c9e04fc3c89d0ede88c9-Paper.pdf
[35]
Yiding Liu, Guan Huang, Jiaxiang Liu, Weixue Lu, Suqi Cheng, Yukun Li, Daiting Shi, Shuaiqiang Wang, Zhicong Cheng, and Dawei Yin. 2021. Pre-trained Language Model for Web-scale Retrieval in Baidu Search. arxiv:2106.03373.
[36]
Zhenghao Liu, Han Zhang, Chenyan Xiong, Zhiyuan Liu, Yu Gu, and Xiaohua Li. 2022. Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder. arxiv:2205.03284.
[37]
Yi Luan, Jacob Eisenstein, Kristina Toutanova, and Michael Collins. 2020. Sparse, Dense, and Attentional Representations for Text Retrieval. CoRR, abs/2005.00181 (2020), arXiv:2005.00181. arxiv:2005.00181
[38]
Xueguang Ma, Liang Wang, Nan Yang, Furu Wei, and Jimmy Lin. 2023. Fine-Tuning LLaMA for Multi-Stage Text Retrieval. arxiv:2310.08319.
[39]
Joel Mackenzie, Andrew Trotman, and Jimmy Lin. 2021. Wacky Weights in Learned Sparse Representations and the Revenge of Score-at-a-Time Query Evaluation. arxiv:2110.11540.
[40]
Yu A. Malkov and D. A. Yashunin. 2020. Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 4 (2020), 824–836. https://doi.org/10.1109/TPAMI.2018.2889473
[41]
Antonio Mallia, Omar Khattab, Torsten Suel, and Nicola Tonellotto. 2021. Learning Passage Impacts for Inverted Indexes. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’21). Association for Computing Machinery, New York, NY, USA. 1723–1727. isbn:9781450380379 https://doi.org/10.1145/3404835.3463030
[42]
Andrzej Maćkiewicz and Waldemar Ratajczak. 1993. Principal components analysis (PCA). Computers & Geosciences, 19, 3 (1993), 303–342. issn:0098-3004 https://doi.org/10.1016/0098-3004(93)90090-R
[43]
Paulius Micikevicius, Dusan Stosic, Neil Burgess, Marius Cornea, Pradeep Dubey, Richard Grisenthwaite, Sangwon Ha, Alexander Heinecke, Patrick Judd, John Kamalu, Naveen Mellempudi, Stuart Oberman, Mohammad Shoeybi, Michael Siu, and Hao Wu. 2022. FP8 Formats for Deep Learning. arxiv:2209.05433.
[44]
2023. TREC 2023 Deep Learning Track. https://microsoft.github.io/msmarco/TREC-Deep-Learning.html
[45]
Marius Muja and David Lowe. 2014. Scalable Nearest Neighbor Algorithms for High Dimensional Data. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36 (2014), 11, 2227–2240. https://doi.org/10.1109/TPAMI.2014.2321376
[46]
Franco Maria Nardini, Cosimo Rulli, and Rossano Venturini. 2024. Efficient Multi-vector Dense Retrieval with Bit Vectors. In Advances in Information Retrieval, Nazli Goharian, Nicola Tonellotto, Yulan He, Aldo Lipani, Graham McDonald, Craig Macdonald, and Iadh Ounis (Eds.). Springer Nature Switzerland, Cham. 3–17. isbn:978-3-031-56060-6
[47]
Rodrigo Frassetto Nogueira, Wei Yang, Kyunghyun Cho, and Jimmy Lin. 2019. Multi-Stage Document Ranking with BERT. CoRR, abs/1910.14424 (2019), arXiv:1910.14424. arxiv:1910.14424
[48]
2023. Nvidia GPUDirect Storage. https://docs.nvidia.com/gpudirect-storage/index.html
[49]
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. CoRR, abs/1802.05365 (2018), arXiv:1802.05365. arxiv:1802.05365
[50]
Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2021. RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Online. 5835–5847. https://doi.org/10.18653/v1/2021.naacl-main.466
[51]
Jie Ren, Minjia Zhang, and Dong Li. 2020. HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.). 33, Curran Associates, Inc., 10672–10684. https://proceedings.neurips.cc/paper_files/paper/2020/file/788d986905533aba051261497ecffcbb-Paper.pdf
[52]
Ruiyang Ren, Yingqi Qu, Jing Liu, Wayne Xin Zhao, QiaoQiao She, Hua Wu, Haifeng Wang, and Ji-Rong Wen. 2021. RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic. 2825–2835. https://doi.org/10.18653/v1/2021.emnlp-main.224
[53]
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. ArXiv, abs/1910.01108 (2019), https://api.semanticscholar.org/CorpusID:203626972
[54]
Keshav Santhanam, Omar Khattab, Christopher Potts, and Matei Zaharia. 2022. PLAID: An Efficient Engine for Late Interaction Retrieval. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM ’22). Association for Computing Machinery, New York, NY, USA. 1747–1756. isbn:9781450392365 https://doi.org/10.1145/3511808.3557325
[55]
Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, and Matei Zaharia. 2022. ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Seattle, United States. 3715–3734. https://doi.org/10.18653/v1/2022.naacl-main.272
[56]
Debendra Das Sharma. 2022. Compute Express Link®: An open industry-standard interconnect enabling heterogeneous data-centric computing. In 2022 IEEE Symposium on High-Performance Interconnects (HOTI). 5–12. https://doi.org/10.1109/HOTI55740.2022.00017
[57]
Anshumali Shrivastava and Ping Li. 2014. Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS). In Advances in Neural Information Processing Systems, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K.Q. Weinberger (Eds.). 27, Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2014/file/310ce61c90f3a46e340ee8257bc70e93-Paper.pdf
[58]
Suhas Jayaram Subramanya, Devvrit, Kadekodi Rohan, Ravishankar Krishnaswamy, and Harsha Simhadri. 2019. DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node. In NeurIPS 2019.
[59]
Chandrahas Tirumalasetty, Chih Chieh Chou, Narasimha Reddy, Paul Gratz, and Ayman Abouelwafa. 2022. Reducing Minor Page Fault Overheads through Enhanced Page Walker. ACM Trans. Archit. Code Optim., 19, 4 (2022), Article 57, sep, 26 pages. issn:1544-3566 https://doi.org/10.1145/3547142
[60]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). 30, Curran Associates, Inc. https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
[61]
Mark Wilkening, Udit Gupta, Samuel Hsia, Caroline Trippel, Carole-Jean Wu, David Brooks, and Gu-Yeon Wei. 2021. RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inference. In Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’21). Association for Computing Machinery, New York, NY, USA. 717–729. isbn:9781450383172 https://doi.org/10.1145/3445814.3446763
[62]
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, and Arnold Overwijk. 2021. Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval. In International Conference on Learning Representations. https://openreview.net/forum?id=zeFrfgyZln
[63]
Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, and Shaoping Ma. 2021. Jointly Optimizing Query Encoder and Product Quantization to Improve Retrieval Performance. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM ’21). Association for Computing Machinery, New York, NY, USA. 2487–2496. isbn:9781450384469 https://doi.org/10.1145/3459637.3482358
[64]
Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, and Shaoping Ma. 2021. Optimizing Dense Retrieval Model Training with Hard Negatives. CoRR, abs/2104.08051 (2021), arXiv:2104.08051. arxiv:2104.08051
[65]
Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, and Shaoping Ma. 2022. Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (WSDM ’22). Association for Computing Machinery, New York, NY, USA. 1328–1336. isbn:9781450391320 https://doi.org/10.1145/3488560.3498443
[66]
Minjia Zhang and Yuxiong He. 2019. GRIP: Multi-Store Capacity-Optimized High-Performance Nearest Neighbor Search for Vector Search Engine. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM ’19). Association for Computing Machinery, New York, NY, USA. 1673–1682. isbn:9781450369763 https://doi.org/10.1145/3357384.3357938
Index Terms
- ESPN: Memory-Efficient Multi-vector Information Retrieval
Recommendations
Multivariate Representation Learning for Information Retrieval
SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information RetrievalDense retrieval models use bi-encoder network architectures for learning query and document representations. These representations are often in the form of a vector representation and their similarities are often computed using the dot product function. ...
Efficient Multi-vector Dense Retrieval with Bit Vectors
Advances in Information RetrievalAbstractDense retrieval techniques employ pre-trained large language models to build a high-dimensional representation of queries and passages. These representations compute the relevance of a passage w.r.t. to a query using efficient similarity ...
Improvement of vector space information retrieval model based on supervised learning
IRAL '00: Proceedings of the fifth international workshop on on Information retrieval with Asian languagesThis paper proposes and method to improve retrieval performance of the vector space model (VSM) by utilizing user-supplied information of those documents that are relevant to the query in question. In addition to the user's relevance feedback ...
Comments
Information & Contributors
Information
Published In
June 2024
141 pages
ISBN:9798400706158
DOI:10.1145/3652024
- General Chair:
- Michael D. Bond,
- Program Chairs:
- Jae W. Lee,
- Hannes Payer
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 20 June 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- Samsung
Conference
ISMM '24
Sponsor:
ISMM '24: 2024 ACM SIGPLAN International Symposium on Memory Management
June 25, 2024
Copenhagen, Denmark
Acceptance Rates
Overall Acceptance Rate 72 of 156 submissions, 46%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 148Total Downloads
- Downloads (Last 12 months)148
- Downloads (Last 6 weeks)88
Reflects downloads up to 12 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in