| Roberta: A robustly optimized bert pretraining approach Y Liu, M Ott, N Goyal, J Du, M Joshi, D Chen, O Levy, M Lewis, ... arXiv preprint arXiv:1907.11692, 2019 | 31698* | 2019 |
| Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension M Lewis, Y Liu, N Goyal, M Ghazvininejad, A Mohamed, O Levy, ... arXiv preprint arXiv:1910.13461, 2019 | 12167 | 2019 |
| GLUE: A multi-task benchmark and analysis platform for natural language understanding A Wang, A Singh, J Michael, F Hill, O Levy, SR Bowman arXiv preprint arXiv:1804.07461, 2018 | 8132 | 2018 |
| Neural word embedding as implicit matrix factorization O Levy, Y Goldberg Advances in neural information processing systems 27, 2014 | 2666 | 2014 |
| Superglue: A stickier benchmark for general-purpose language understanding systems A Wang, Y Pruksachatkun, N Nangia, A Singh, J Michael, F Hill, O Levy, ... Advances in neural information processing systems 32, 2019 | 2523 | 2019 |
| word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method Y Goldberg, O Levy arXiv preprint arXiv:1402.3722, 2014 | 2476 | 2014 |
| Spanbert: Improving pre-training by representing and predicting spans M Joshi, D Chen, Y Liu, DS Weld, L Zettlemoyer, O Levy Transactions of the association for computational linguistics 8, 64-77, 2020 | 2343 | 2020 |
| What does bert look at? an analysis of bert's attention K Clark, U Khandelwal, O Levy, CD Manning arXiv preprint arXiv:1906.04341, 2019 | 1987 | 2019 |
| Improving distributional similarity with lessons learned from word embeddings O Levy, Y Goldberg, I Dagan Transactions of the association for computational linguistics 3, 211-225, 2015 | 1919 | 2015 |
| Dependency-Based Word Embeddings O Levy, Y Goldberg Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 1769 | 2014 |
| code2vec: Learning distributed representations of code U Alon, M Zilberstein, O Levy, E Yahav Proceedings of the ACM on Programming Languages 3 (POPL), 1-29, 2019 | 1540 | 2019 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 1393 | 2022 |
| Annotation artifacts in natural language inference data S Gururangan, S Swayamdipta, O Levy, R Schwartz, SR Bowman, ... arXiv preprint arXiv:1803.02324, 2018 | 1301 | 2018 |
| Are sixteen heads really better than one? P Michel, O Levy, G Neubig Advances in neural information processing systems 32, 2019 | 1177 | 2019 |
| Lima: Less is more for alignment C Zhou, P Liu, P Xu, S Iyer, J Sun, Y Mao, X Ma, A Efrat, P Yu, L Yu, ... Advances in Neural Information Processing Systems 36, 55006-55021, 2023 | 1000 | 2023 |
| code2seq: Generating sequences from structured representations of code U Alon, S Brody, O Levy, E Yahav arXiv preprint arXiv:1808.01400, 2018 | 897 | 2018 |
| Generalization through memorization: Nearest neighbor language models U Khandelwal, O Levy, D Jurafsky, L Zettlemoyer, M Lewis arXiv preprint arXiv:1911.00172, 2019 | 885 | 2019 |
| Linguistic Regularities in Sparse and Explicit Word Representations O Levy, Y Goldberg Proceedings of the Eighteenth Conference on Computational Natural Language …, 2014 | 837 | 2014 |
| Transformer feed-forward layers are key-value memories M Geva, R Schuster, J Berant, O Levy arXiv preprint arXiv:2012.14913, 2020 | 692 | 2020 |
| Zero-shot relation extraction via reading comprehension O Levy, M Seo, E Choi, L Zettlemoyer arXiv preprint arXiv:1706.04115, 2017 | 658 | 2017 |
Luke ZettlemoyerUniversity of Washington; MetaVerified email at cs.washington.edu