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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, T. Rocktäschel, S. Riedel, and D. Kiela. Advances in Neural Information Processing Systems, 33, page 9459--9474. Curran Associates, Inc., (2020)

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Vladimir Rubinov

Vladimir Legradi

Vladimir Spasitsch

Vladimir Dyakonov

Vladimir Spokoiny

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Joint Verification and Reranking for Open Fact Checking Over Tables.M. Schlichtkrull, V. Karpukhin, B. Oguz, M. Lewis, W. tau Yih, and S. Riedel. CoRR, (2020)Domain-matched Pre-training Tasks for Dense Retrieval.B. Oguz, K. Lakhotia, A. Gupta, P. Lewis, V. Karpukhin, A. Piktus, X. Chen, S. Riedel, S. Yih, S. Gupta and 1 other author(s). NAACL-HLT (Findings), page 1524-1534. Association for Computational Linguistics, (2022)Domain-matched Pre-training Tasks for Dense Retrieval.B. Oguz, K. Lakhotia, A. Gupta, P. Lewis, V. Karpukhin, A. Piktus, X. Chen, S. Riedel, W. tau Yih, S. Gupta and 1 other author(s). CoRR, (2021)Unified Open-Domain Question Answering with Structured and Unstructured Knowledge.B. Oguz, X. Chen, V. Karpukhin, S. Peshterliev, D. Okhonko, M. Schlichtkrull, S. Gupta, Y. Mehdad, and S. Yih. CoRR, (2020)KILT: a Benchmark for Knowledge Intensive Language Tasks.F. Petroni, A. Piktus, A. Fan, P. Lewis, M. Yazdani, N. Cao, J. Thorne, Y. Jernite, V. Karpukhin, J. Maillard and 3 other author(s). NAACL-HLT, page 2523-2544. Association for Computational Linguistics, (2021)Dense Passage Retrieval for Open-Domain Question AnsweringV. Karpukhin, B. Oğuz, S. Min, P. Lewis, L. Wu, S. Edunov, D. Chen, and W. Yih. (2020)cite arxiv:2004.04906Comment: EMNLP 2020.Discourse-Aware Prompt Design for Text Generation.M. Ghazvininejad, V. Karpukhin, and A. Celikyilmaz. CoRR, (2021)CM3: A Causal Masked Multimodal Model of the Internet.A. Aghajanyan, B. Huang, C. Ross, V. Karpukhin, H. Xu, N. Goyal, D. Okhonko, M. Joshi, G. Ghosh, M. Lewis and 1 other author(s). CoRR, (2022)UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering.B. Oguz, X. Chen, V. Karpukhin, S. Peshterliev, D. Okhonko, M. Schlichtkrull, S. Gupta, Y. Mehdad, and S. Yih. NAACL-HLT (Findings), page 1535-1546. Association for Computational Linguistics, (2022)NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned.S. Min, J. Boyd-Graber, C. Alberti, D. Chen, E. Choi, M. Collins, K. Guu, H. Hajishirzi, K. Lee, J. Palomaki and 43 other author(s). NeurIPS (Competition and Demos), volume 133 of Proceedings of Machine Learning Research, page 86-111. PMLR, (2020)

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Disambiguation of "Karpukhin, Vladimir"

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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Please choose a person to relate this publication to

Vladimir Rubinov

Vladimir Legradi

Vladimir Spasitsch

Vladimir Dyakonov

Vladimir Spokoiny

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Karpukhin, Vladimir"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Please choose a person to relate this publication to

Vladimir Rubinov

Vladimir Legradi

Vladimir Spasitsch

Vladimir Dyakonov

Vladimir Spokoiny

Other publications of authors with the same name

Disambiguation

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URL
DOI
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Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks