| Large Language Models are Zero-Shot Reasoners T Kojima, SS Gu, M Reid, Y Matsuo, Y Iwasawa NeurIPS 2022, 2022 | 3571 | 2022 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2192 | 2023 |
| Gemma: Open models based on gemini research and technology G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ... arXiv preprint arXiv:2403.08295, 2024 | 724 | 2024 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 689 | 2024 |
| Gemma 2: Improving open language models at a practical size G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... arXiv preprint arXiv:2408.00118, 2024 | 193 | 2024 |
| Can wikipedia help offline reinforcement learning? M Reid, Y Yamada, SS Gu arXiv preprint arXiv:2201.12122, 2022 | 97 | 2022 |
| LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer M Reid, V Zhong Findings of the Annual Meeting of the Association for Computational …, 2021 | 71 | 2021 |
| Diffuser: Diffusion via edit-based reconstruction M Reid, VJ Hellendoorn, G Neubig The Eleventh International Conference on Learning Representations, 2023 | 54* | 2023 |
| A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation DI Adelani, JO Alabi, A Fan, J Kreutzer, X Shen, M Reid, D Ruiter, ... NAACL 2022, 2022 | 53* | 2022 |
| Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers M Reid, E Marrese-Taylor, Y Matsuo Findings of Empirical Methods in Natural Language Processing (EMNLP), 2021 | 47 | 2021 |
| Learning to Model Editing Processes M Reid, G Neubig Findings of Empirical Methods in Natural Language Processing (EMNLP), 2022 | 34 | 2022 |
| AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages M Reid, J Hu, G Neubig, Y Matsuo Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021 | 29 | 2021 |
| VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling M Reid, E Marrese-Taylor, Y Matsuo Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 | 23 | 2020 |
| M2D2: A Massively Multi-domain Language Modeling Dataset M Reid, V Zhong, S Gururangan, L Zettlemoyer Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022 | 20 | 2022 |
| PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining M Reid, M Artetxe Conference of the North American Chapter of the Association for …, 2021 | 20 | 2021 |
| mmt5: Modular multilingual pre-training solves source language hallucinations J Pfeiffer, F Piccinno, M Nicosia, X Wang, M Reid, S Ruder arXiv preprint arXiv:2305.14224, 2023 | 17 | 2023 |
| Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining F Zheng, M Reid, E Marrese-Taylor, Y Matsuo AmericasNLP Workshop, NAACL 2021, 2021 | 14 | 2021 |
| On the impact of data augmentation on downstream performance in natural language processing I Okimura, M Reid, M Kawano, Y Matsuo Proceedings of the third workshop on insights from negative results in NLP …, 2022 | 13 | 2022 |
| Buffet: Benchmarking large language models for few-shot cross-lingual transfer A Asai, S Kudugunta, XV Yu, T Blevins, H Gonen, M Reid, Y Tsvetkov, ... arXiv preprint arXiv:2305.14857, 2023 | 12 | 2023 |
| Variational Inference for Learning Representations of Natural Language Edits E Marrese-Taylor, M Reid, Y Matsuo AAAI 2021, 2020 | 8 | 2020 |
Yutaka MatsuoProfessor, University of TokyoVerified email at weblab.t.u-tokyo.ac.jp
