| Chain of thought prompting elicits reasoning in large language models J Wei, X Wang, D Schuurmans, M Bosma, E Chi, Q Le, D Zhou Neural Information Processing Systems (NeurIPS), 2022, 2022 | 11844* | 2022 |
| Palm: Scaling language modeling with pathways A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ... Journal of Machine Learning Research (JMLR), 2023, 2023 | 5726 | 2023 |
| Scaling instruction-finetuned language models HW Chung, L Hou, S Longpre, B Zoph, Y Tay, W Fedus, Y Li, X Wang, ... JMLR 2024, 2024 | 3464 | 2024 |
| Self-consistency improves chain of thought reasoning in language models X Wang, J Wei, D Schuurmans, Q Le, E Chi, S Narang, A Chowdhery, ... ICLR 2023, 2023 | 2835* | 2023 |
| 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 | 2494 | 2023 |
| Palm 2 technical report R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ... arXiv preprint arXiv:2305.10403, 2023 | 1568 | 2023 |
| Least-to-most prompting enables complex reasoning in large language models D Zhou, N Schärli, L Hou, J Wei, N Scales, X Wang, D Schuurmans, ... ICLR 2023, 2023 | 1232 | 2023 |
| 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 | 966 | 2024 |
| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... Journal of Machine Learning Research 23 (226), 1-61, 2022 | 844 | 2022 |
| Large language models as optimizers C Yang, X Wang, Y Lu, H Liu, QV Le, D Zhou, X Chen ICLR 2024, 2024 | 657* | 2024 |
| Large language models can self-improve J Huang, SS Gu, L Hou, Y Wu, X Wang, H Yu, J Han EMNLP 2023, 2023 | 490 | 2023 |
| Fairness without demographics through adversarially reweighted learning P Lahoti, A Beutel, J Chen, K Lee, F Prost, N Thain, X Wang, EH Chi 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 384 | 2020 |
| ToTTo: A Controlled Table-To-Text Generation Dataset AP Parikh, X Wang, S Gehrmann, M Faruqui, B Dhingra, D Yang, D Das EMNLP 2020, 2020 | 374 | 2020 |
| Unifying Language Learning Paradigms Y Tay, M Dehghani, VQ Tran, X Garcia, J Wei, X Wang, HW Chung, ... ICLR 2023, 2023 | 314* | 2023 |
| Language models are multilingual chain-of-thought reasoners F Shi, M Suzgun, M Freitag, X Wang, S Srivats, S Vosoughi, HW Chung, ... ICLR 2023, 2023 | 272 | 2023 |
| ESCAPES: evacuation simulation with children, authorities, parents, emotions, and social comparison. J Tsai, N Fridman, E Bowring, M Brown, S Epstein, GA Kaminka, ... AAMAS 11, 457-464, 2011 | 258 | 2011 |
| Measuring and reducing gendered correlations in pre-trained models K Webster, X Wang, I Tenney, A Beutel, E Pitler, E Pavlick, J Chen, E Chi, ... arXiv preprint arXiv:2010.06032, 2020 | 164 | 2020 |
| Large language models as tool makers T Cai, X Wang, T Ma, X Chen, D Zhou ICLR 2024, 2024 | 154 | 2024 |
| Freshllms: Refreshing large language models with search engine augmentation T Vu, M Iyyer, X Wang, N Constant, J Wei, J Wei, C Tar, YH Sung, D Zhou, ... ACL 2024 Findings, 2024 | 147 | 2024 |
| Measure and Improve Robustness in NLP Models: A Survey X Wang, H Wang, D Yang NAACL 2022, 2022 | 130 | 2022 |
Denny ZhouResearch Scientist, Google DeepMind確認したメール アドレス: google.com