| Large Language Models are Zero-Shot Reasoners T Kojima, SS Gu, M Reid, Y Matsuo, Y Iwasawa NeurIPS 2022, 2022 | 7645 | 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 | 7058 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 3388 | 2024 |
| 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 | 2305 | 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 | 1712 | 2024 |
| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities G Comanici, E Bieber, M Schaekermann, I Pasupat, N Sachdeva, I Dhillon, ... arXiv preprint arXiv:2507.06261, 2025 | 1259 | 2025 |
| Can wikipedia help offline reinforcement learning? M Reid, Y Yamada, SS Gu arXiv preprint arXiv:2201.12122, 2022 | 118 | 2022 |
| 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 | 95* | 2022 |
| Diffuser: Discrete diffusion via edit-based reconstruction M Reid, VJ Hellendoorn, G Neubig ICLR 2023, 2022 | 82 | 2022 |
| LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer M Reid, V Zhong Findings of the Annual Meeting of the Association for Computational …, 2021 | 81 | 2021 |
| 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 | 61 | 2021 |
| 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, ... NAACL 2023, 2023 | 45 | 2023 |
| Learning to Model Editing Processes M Reid, G Neubig Findings of Empirical Methods in Natural Language Processing (EMNLP), 2022 | 38 | 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 | 36 | 2021 |
| mmT5: Modular multilingual pre-training solves source language hallucinations J Pfeiffer, F Piccinno, M Nicosia, X Wang, M Reid, S Ruder EMNLP Findings 2023, 2023 | 27 | 2023 |
| 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 | 27 | 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 | 27 | 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 | 25 | 2022 |
| 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 | 25 | 2022 |
| Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining F Zheng, M Reid, E Marrese-Taylor, Y Matsuo AmericasNLP Workshop, NAACL 2021, 2021 | 19 | 2021 |
Yutaka MatsuoProfessor, University of TokyoVerified email at weblab.t.u-tokyo.ac.jp
