| 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 | 3310 | 2020 |
| MLQA: Evaluating cross-lingual extractive question answering P Lewis, B Oğuz, R Rinott, S Riedel, H Schwenk arXiv preprint arXiv:1910.07475, 2019 | 476 | 2019 |
| Llm-qat: Data-free quantization aware training for large language models Z Liu, B Oguz, C Zhao, E Chang, P Stock, Y Mehdad, Y Shi, ... arXiv preprint arXiv:2305.17888, 2023 | 192 | 2023 |
| 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 | 144* | 2020 |
| Effective long-context scaling of foundation models W Xiong, J Liu, I Molybog, H Zhang, P Bhargava, R Hou, L Martin, ... arXiv preprint arXiv:2309.16039, 2023 | 137 | 2023 |
| 3dgen: Triplane latent diffusion for textured mesh generation A Gupta, W Xiong, Y Nie, I Jones, B Oğuz arXiv preprint arXiv:2303.05371, 2023 | 121 | 2023 |
| 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 | 74 | 2021 |
| Chameleon: Mixed-modal early-fusion foundation models C Team arXiv preprint arXiv:2405.09818, 2024 | 73 | 2024 |
| How to train your dragon: Diverse augmentation towards generalizable dense retrieval SC Lin, A Asai, M Li, B Oguz, J Lin, Y Mehdad, W Yih, X Chen arXiv preprint arXiv:2302.07452, 2023 | 69 | 2023 |
| 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 | 60 | 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 | 60 | 2021 |
| Salient phrase aware dense retrieval: can a dense retriever imitate a sparse one? X Chen, K Lakhotia, B Oğuz, A Gupta, P Lewis, S Peshterliev, Y Mehdad, ... arXiv preprint arXiv:2110.06918, 2021 | 59 | 2021 |
| Answering complex open-domain questions with multi-hop dense retrieval W Xiong, XL Li, S Iyer, J Du, P Lewis, WY Wang, Y Mehdad, W Yih, ... arXiv preprint arXiv:2009.12756, 2020 | 58 | 2020 |
| Bit: Robustly binarized multi-distilled transformer Z Liu, B Oguz, A Pappu, L Xiao, S Yih, M Li, R Krishnamoorthi, Y Mehdad Advances in neural information processing systems 35, 14303-14316, 2022 | 55 | 2022 |
| 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 | 49 | 2021 |
| Hierarchical video-moment retrieval and step-captioning A Zala, J Cho, S Kottur, X Chen, B Oguz, Y Mehdad, M Bansal Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 41 | 2023 |
| Accelerating recurrent neural network training via two stage classes and parallelization Z Huang, G Zweig, M Levit, B Dumoulin, B Oguz, S Chang 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, 326-331, 2013 | 38 | 2013 |
| Multilingual seq2seq training with similarity loss for cross-lingual document classification K Yu, H Li, B Oguz Proceedings of the third workshop on representation learning for NLP, 175-179, 2018 | 35 | 2018 |
| 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 |
| Simple local attentions remain competitive for long-context tasks W Xiong, B Oğuz, A Gupta, X Chen, D Liskovich, O Levy, W Yih, ... arXiv preprint arXiv:2112.07210, 2021 | 24 | 2021 |