| Transformers: State-of-the-art natural language processing T Wolf, L Debut, V Sanh, J Chaumond, C Delangue, A Moi, P Cistac, ... Proceedings of the 2020 conference on empirical methods in natural language …, 2020 | 12746* | 2020 |
| A neural attention model for abstractive sentence summarization AM Rush, S Chopra, J Weston arXiv preprint arXiv:1509.00685, 2015 | 3796 | 2015 |
| Opennmt: Open-source toolkit for neural machine translation G Klein, Y Kim, Y Deng, J Senellart, AM Rush arXiv preprint arXiv:1701.02810, 2017 | 2473 | 2017 |
| Multitask prompted training enables zero-shot task generalization V Sanh, A Webson, C Raffel, SH Bach, L Sutawika, Z Alyafeai, A Chaffin, ... arXiv preprint arXiv:2110.08207, 2021 | 2343 | 2021 |
| Character-aware neural language models Y Kim, Y Jernite, D Sontag, A Rush Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 2332 | 2016 |
| Bloom: A 176b-parameter open-access multilingual language model BS Workshop, TL Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, ... arXiv preprint arXiv:2211.05100, 2022 | 2291 | 2022 |
| Towards ai-complete question answering: A set of prerequisite toy tasks J Weston, A Bordes, S Chopra, AM Rush, B Van Merriënboer, A Joulin, ... arXiv preprint arXiv:1502.05698, 2015 | 1430 | 2015 |
| Sequence-level knowledge distillation Y Kim, AM Rush Proceedings of the 2016 conference on empirical methods in natural language …, 2016 | 1377 | 2016 |
| Abstractive sentence summarization with attentive recurrent neural networks S Chopra, M Auli, AM Rush Proceedings of the 2016 conference of the North American chapter of the …, 2016 | 1273 | 2016 |
| Bottom-up abstractive summarization S Gehrmann, Y Deng, AM Rush arXiv preprint arXiv:1808.10792, 2018 | 924 | 2018 |
| Gltr: Statistical detection and visualization of generated text S Gehrmann, H Strobelt, AM Rush arXiv preprint arXiv:1906.04043, 2019 | 896 | 2019 |
| Zephyr: Direct distillation of lm alignment L Tunstall, E Beeching, N Lambert, N Rajani, K Rasul, Y Belkada, ... arXiv preprint arXiv:2310.16944, 2023 | 830 | 2023 |
| Challenges in data-to-document generation S Wiseman, SM Shieber, AM Rush arXiv preprint arXiv:1707.08052, 2017 | 770 | 2017 |
| Sequence-to-sequence learning as beam-search optimization S Wiseman, AM Rush arXiv preprint arXiv:1606.02960, 2016 | 735 | 2016 |
| Structured attention networks Y Kim, C Denton, L Hoang, AM Rush arXiv preprint arXiv:1702.00887, 2017 | 709 | 2017 |
| Movement pruning: Adaptive sparsity by fine-tuning V Sanh, T Wolf, A Rush Advances in neural information processing systems 33, 20378-20389, 2020 | 655 | 2020 |
| Lstmvis: A tool for visual analysis of hidden state dynamics in recurrent neural networks H Strobelt, S Gehrmann, H Pfister, AM Rush IEEE transactions on visualization and computer graphics 24 (1), 667-676, 2017 | 619 | 2017 |
| Parameter-efficient transfer learning with diff pruning D Guo, AM Rush, Y Kim Proceedings of the 59th annual meeting of the association for computational …, 2021 | 550 | 2021 |
| Datasets: A community library for natural language processing Q Lhoest, AV Del Moral, Y Jernite, A Thakur, P Von Platen, S Patil, ... Proceedings of the 2021 conference on empirical methods in natural language …, 2021 | 486 | 2021 |
| Scaling data-constrained language models N Muennighoff, A Rush, B Barak, T Le Scao, N Tazi, A Piktus, S Pyysalo, ... Advances in Neural Information Processing Systems 36, 50358-50376, 2023 | 430 | 2023 |
Yoon KimAssociate Professor, MITVerified email at mit.edu
