Levy, Omer

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv

Digital Humanities and ScholarshipComputational and Text Analysis Methods

Cited by 16,515

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 8,821

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 8,816

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Topic ModelingNatural Language Processing Techniques

Cited by 4,222

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., & Levy, O. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,827

SpanBERT: Improving Pre-training by Representing and Predicting Spans

Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., & Levy, O. (2020) · Transactions of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 1,827

SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

Wang, A., Pruksachatkun, Y., Nangia, N., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1,051

Annotation Artifacts in Natural Language Inference Data

Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S., & Smith, N. A. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 907

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. R. (2018) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 562

Zero-Shot Relation Extraction via Reading Comprehension

Levy, O., Seo, M., Choi, E., & Zettlemoyer, L. (2017) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 482

Zero-Shot Relation Extraction via Reading Comprehension

Levy, O., Seo, M., Choi, E., & Zettlemoyer, L. (2017) · Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

Topic ModelingNatural Language Processing Techniques

Cited by 482

Mask-Predict: Parallel Decoding of Conditional Masked Language Models

Ghazvininejad, M., Levy, O., Liu, Y., & Zettlemoyer, L. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Natural Language Processing TechniquesTopic Modeling

Cited by 464

Shared computational principles for language processing in humans and deep language models

Goldstein, A., Zada, Z., Buchnik, E., Schain, M., Price, A., Aubrey, B., Nastase, S. A., Feder, A., Emanuel, D., Cohen, A., et al. (2022) · Nature Neuroscience

Neurobiology of Language and BilingualismReading and Literacy Development

Cited by 447

Are Sixteen Heads Really Better than One?

Michel, P., Levy, O., & Neubig, G. (2019) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 404

Transformer Feed-Forward Layers Are Key-Value Memories

Geva, M., Schuster, R., Berant, J., & Levy, O. (2020) · arXiv

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 353

Transformer Feed-Forward Layers Are Key-Value Memories

Geva, M., Schuster, R., Berant, J., & Levy, O. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 353

Generalization through Memorization: Nearest Neighbor Language Models

Khandelwal, U., Levy, O., Jurafsky, D., Zettlemoyer, L., & Lewis, M. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 285

LIMA: Less Is More for Alignment

Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., et al. (2023) · Advances in Neural Information Processing Systems 36

Domain Adaptation and Few-Shot LearningEducational Assessment and Pedagogy

Cited by 275

LIMA: Less Is More for Alignment

Zhou, C., Liu, P., Xu, P., Iyer, S., Sun, J., Mao, Y., Ma, X., Efrat, A., Yu, P., Yu, L., et al. (2023) · arXiv

Domain Adaptation and Few-Shot LearningEducational Assessment and Pedagogy

Cited by 275

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Improving Transformer Models by Reordering their Sublayers

Press, O., Smith, N. A., & Levy, O. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 64

Coreference Resolution without Span Representations

Kirstain, Y., Ram, O., & Levy, O. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Natural Language Processing TechniquesTopic Modeling

Cited by 60

SCROLLS: Standardized CompaRison Over Long Language Sequences

Shaham, U., Segal, E., Ivgi, M., Efrat, A., Yoran, O., Haviv, A., Gupta, A., Xiong, W., Geva, M., Berant, J., et al. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 40