| 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 | 32295* | 2019 |
| Deep contextualized word representations ME Peters, M Neumann, M Iyyer, M Gardner, C Clark, K Lee, ... NAACL, 2018 | 16776* | 2018 |
| 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 | 12272 | 2019 |
| Unsupervised cross-lingual representation learning at scale A Conneau, K Khandelwal, N Goyal, V Chaudhary, G Wenzek, F Guzmán, ... arXiv preprint arXiv:1911.02116, 2019 | 7082 | 2019 |
| Opt: Open pre-trained transformer language models S Zhang, S Roller, N Goyal, M Artetxe, M Chen, S Chen, C Dewan, ... arXiv preprint arXiv:2205.01068, 2022 | 3821* | 2022 |
| Qlora: Efficient finetuning of quantized llms T Dettmers, A Pagnoni, A Holtzman, L Zettlemoyer Advances in neural information processing systems 36, 10088-10115, 2023 | 2649 | 2023 |
| Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension M Joshi, E Choi, DS Weld, L Zettlemoyer arXiv preprint arXiv:1705.03551, 2017 | 2556 | 2017 |
| 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 | 2339 | 2020 |
| Multilingual denoising pre-training for neural machine translation Y Liu, J Gu, N Goyal, X Li, S Edunov, M Ghazvininejad, M Lewis, ... Transactions of the Association for Computational Linguistics 8, 726-742, 2020 | 1984 | 2020 |
| Toolformer: Language models can teach themselves to use tools T Schick, J Dwivedi-Yu, R Dessì, R Raileanu, M Lomeli, E Hambro, ... Advances in Neural Information Processing Systems 36, 68539-68551, 2023 | 1578 | 2023 |
| Allennlp: A deep semantic natural language processing platform M Gardner, J Grus, M Neumann, O Tafjord, P Dasigi, N Liu, M Peters, ... arXiv preprint arXiv:1803.07640, 2018 | 1463 | 2018 |
| Rethinking the role of demonstrations: What makes in-context learning work? S Min, X Lyu, A Holtzman, M Artetxe, M Lewis, H Hajishirzi, L Zettlemoyer arXiv preprint arXiv:2202.12837, 2022 | 1338 | 2022 |
| Knowledge-based weak supervision for information extraction of overlapping relations R Hoffmann, C Zhang, X Ling, L Zettlemoyer, DS Weld Proceedings of the 49th annual meeting of the association for computational …, 2011 | 1258 | 2011 |
| End-to-end neural coreference resolution K Lee, L He, M Lewis, L Zettlemoyer arXiv preprint arXiv:1707.07045, 2017 | 1212 | 2017 |
| Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars LS Zettlemoyer, M Collins Conference on Uncertainty in Artificial Intelligence (UAI), 2005 | 1172* | 2005 |
| Gpt3. int8 (): 8-bit matrix multiplication for transformers at scale T Dettmers, M Lewis, Y Belkada, L Zettlemoyer Advances in neural information processing systems 35, 30318-30332, 2022 | 1045 | 2022 |
| 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 | 1008 | 2023 |
| QuAC: Question answering in context E Choi, H He, M Iyyer, M Yatskar, W Yih, Y Choi, P Liang, L Zettlemoyer arXiv preprint arXiv:1808.07036, 2018 | 983 | 2018 |
| Generalization through memorization: Nearest neighbor language models U Khandelwal, O Levy, D Jurafsky, L Zettlemoyer, M Lewis arXiv preprint arXiv:1911.00172, 2019 | 888 | 2019 |
| Summarizing source code using a neural attention model S Iyer, I Konstas, A Cheung, L Zettlemoyer 54th Annual Meeting of the Association for Computational Linguistics 2016 …, 2016 | 886 | 2016 |
Mike LewisFacebook AI Research確認したメール アドレス: fb.com
