Retrieval-augmented generation for knowledge-intensive nlp tasks P Lewis, E Perez, A Piktus, F Petroni, V Karpukhin, N Goyal, H Küttler, ... Advances in Neural Information Processing Systems 33, 9459-9474, 2020 | 3649 | 2020 |
Dense passage retrieval for open-domain question answering V Karpukhin, B Oğuz, S Min, P Lewis, L Wu, S Edunov, D Chen, W Yih arXiv preprint arXiv:2004.04906, 2020 | 2920 | 2020 |
KILT: a benchmark for knowledge intensive language tasks F Petroni, A Piktus, A Fan, P Lewis, M Yazdani, N De Cao, J Thorne, ... arXiv preprint arXiv:2009.02252, 2020 | 462 | 2020 |
Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen tau Yih. 2020 V Karpukhin, B Oguz Dense passage retrieval for opendomain question answering, 2004 | 144 | 2004 |
Cm3: A causal masked multimodal model of the internet A Aghajanyan, B Huang, C Ross, V Karpukhin, H Xu, N Goyal, D Okhonko, ... arXiv preprint arXiv:2201.07520, 2022 | 141 | 2022 |
Aligned cross entropy for non-autoregressive machine translation M Ghazvininejad, V Karpukhin, L Zettlemoyer, O Levy International Conference on Machine Learning, 3515-3523, 2020 | 112 | 2020 |
Training on synthetic noise improves robustness to natural noise in machine translation V Karpukhin, O Levy, J Eisenstein, M Ghazvininejad Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019), 42-47, 2019 | 106 | 2019 |
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, ... arXiv preprint arXiv:2012.14610, 2020 | 101 | 2020 |
Neurips 2020 efficientqa competition: Systems, analyses and lessons learned S Min, J Boyd-Graber, C Alberti, D Chen, E Choi, M Collins, K Guu, ... NeurIPS 2020 Competition and Demonstration Track, 86-111, 2021 | 73 | 2021 |
Multi-task retrieval for knowledge-intensive tasks J Maillard, V Karpukhin, F Petroni, W Yih, B Oğuz, V Stoyanov, G Ghosh arXiv preprint arXiv:2101.00117, 2021 | 59 | 2021 |
Domain-matched pre-training tasks for dense retrieval B Oğuz, K Lakhotia, A Gupta, P Lewis, V Karpukhin, A Piktus, X Chen, ... arXiv preprint arXiv:2107.13602, 2021 | 57 | 2021 |
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. arXiv 2005 PSH Lewis, E Perez, A Piktus, F Petroni, V Karpukhin, N Goyal, H Küttler, ... arXiv preprint arXiv:2005.11401, 0 | 56 | |
The web is your oyster-knowledge-intensive NLP against a very large web corpus A Piktus, F Petroni, V Karpukhin, D Okhonko, S Broscheit, G Izacard, ... arXiv preprint arXiv:2112.09924, 2021 | 44 | 2021 |
Unified open-domain question answering with structured and unstructured knowledge B Oguz, X Chen, V Karpukhin, S Peshterliev, D Okhonko, M Schlichtkrull, ... arXiv preprint arXiv:2012.14610, 2020 | 35 | 2020 |
Joint verification and reranking for open fact checking over tables M Schlichtkrull, V Karpukhin, B Oğuz, M Lewis, W Yih, S Riedel arXiv preprint arXiv:2012.15115, 2020 | 25 | 2020 |
Sewon Min, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 V Karpukhin, B Oguz Dense passage retrieval for open-domain question answering. CoRR, abs, 2004 | 20 | 2004 |
Nonparametric decoding for generative retrieval H Lee, J Kim, H Chang, H Oh, S Yang, V Karpukhin, Y Lu, M Seo arXiv preprint arXiv:2210.02068, 2022 | 14 | 2022 |
Discourse-aware soft prompting for text generation M Ghazvininejad, V Karpukhin, V Gor, A Celikyilmaz arXiv preprint arXiv:2112.05717, 2021 | 5 | 2021 |
Discourse-aware prompt design for text generation M Ghazvininejad, V Karpukhin, A Celikyilmaz arXiv preprint arXiv:2112.05717, 2021 | 4 | 2021 |