| 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 | 2487* | 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 | 2229 | 2023 |
| Scaling instruction-finetuned language models HW Chung, L Hou, S Longpre, B Zoph, Y Tay, W Fedus, Y Li, X Wang, ... arXiv preprint arXiv:2210.11416, 2022 | 920 | 2022 |
| 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 | 676* | 2023 |
| Underspecification presents challenges for credibility in modern machine learning A D'Amour, K Heller, D Moldovan, B Adlam, B Alipanahi, A Beutel, ... The Journal of Machine Learning Research 23 (1), 10237-10297, 2022 | 598 | 2022 |
| 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 | 374 | 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 | 313 | 2023 |
| 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 | 264 | 2020 |
| 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 | 247 | 2020 |
| 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 | 246 | 2011 |
| Unifying Language Learning Paradigms Y Tay, M Dehghani, VQ Tran, X Garcia, J Wei, X Wang, HW Chung, ... ICLR 2023, 2023 | 175* | 2023 |
| Large language models can self-improve J Huang, SS Gu, L Hou, Y Wu, X Wang, H Yu, J Han EMNLP 2023, 2023 | 131 | 2023 |
| 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 | 124 | 2020 |
| Adana: Active name disambiguation X Wang, J Tang, H Cheng, SY Philip 2011 IEEE 11th international conference on data mining, 794-803, 2011 | 111 | 2011 |
| Flexible transfer learning under support and model shift X Wang, J Schneider Advances in Neural Information Processing Systems 27, 2014 | 101 | 2014 |
| Active transfer learning under model shift X Wang, TK Huang, J Schneider International Conference on Machine Learning, 1305-1313, 2014 | 96 | 2014 |
| Biperpedia: An ontology for search applications R Gupta, A Halevy, X Wang, S Whang, F Wu VLDB 2014, 2014 | 93 | 2014 |
| 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 | 70 | 2023 |
| Relevant document discovery for fact-checking articles X Wang, C Yu, S Baumgartner, F Korn Companion Proceedings of the The Web Conference 2018, 525-533, 2018 | 70 | 2018 |
| CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation T Wang, X Wang, Y Qin, B Packer, K Li, J Chen, A Beutel, E Chi EMNLP 2020, 2020 | 68 | 2020 |
Jason WeiOpenAIVerified email at google.com
