| On the variance of the adaptive learning rate and beyond L Liu, H Jiang, P He, W Chen, X Liu, J Gao, J Han ICLR 2019, 2019 | 1522 | 2019 |
| Multi-task deep neural networks for natural language understanding X Liu, P He, W Chen, J Gao ACL 2019, 2019 | 1093 | 2019 |
| Deberta: Decoding-enhanced bert with disentangled attention P He, X Liu, J Gao, W Chen ICLR 2021, 2020 | 948 | 2020 |
| Smart: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization H Jiang, P He, W Chen, X Liu, J Gao, T Zhao ACL 2020, 2019 | 288 | 2019 |
| Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing P He, J Gao, W Chen ICLR 2023, 2021 | 157 | 2021 |
| Improving multi-task deep neural networks via knowledge distillation for natural language understanding X Liu, P He, W Chen, J Gao arXiv preprint arXiv:1904.09482, 2019 | 142 | 2019 |
| Adversarial training for large neural language models X Liu, H Cheng, P He, W Chen, Y Wang, H Poon, J Gao arXiv preprint arXiv:2004.08994, 2020 | 102 | 2020 |
| Generation-augmented retrieval for open-domain question answering Y Mao, P He, X Liu, Y Shen, J Gao, J Han, W Chen arXiv preprint arXiv:2009.08553, 2020 | 92 | 2020 |
| X-SQL: reinforce schema representation with context P He, Y Mao, K Chakrabarti, W Chen arXiv preprint arXiv:1908.08113, 2019 | 74 | 2019 |
| On the variance of the adaptive learning rate and beyond. arXiv 2019 L Liu, H Jiang, P He, W Chen, X Liu, J Gao, J Han arXiv preprint arXiv:1908.03265, 2019 | 55 | 2019 |
| NeurIPS 2020 EfficientQA competition: Systems, analyses and lessons learned S Min, J Boyd-Graber, C Alberti, D Chen, E Choi, M Collins, K Guu, ... NeurIPS 2020, 2021 | 52 | 2021 |
| The microsoft toolkit of multi-task deep neural networks for natural language understanding X Liu, Y Wang, J Ji, H Cheng, X Zhu, E Awa, P He, W Chen, H Poon, ... arXiv preprint arXiv:2002.07972, 2020 | 42 | 2020 |
| Exploiting structured knowledge in text via graph-guided representation learning T Shen, Y Mao, P He, G Long, A Trischler, W Chen arXiv preprint arXiv:2004.14224, 2020 | 39 | 2020 |
| Diffusion-GAN: Training GANs with Diffusion Z Wang, H Zheng, P He, W Chen, M Zhou ICLR 2023, 2022 | 33 | 2022 |
| A hybrid neural network model for commonsense reasoning P He, X Liu, W Chen, J Gao arXiv preprint arXiv:1907.11983, 2019 | 30 | 2019 |
| Instruction tuning with gpt-4 B Peng, C Li, P He, M Galley, J Gao arXiv preprint arXiv:2304.03277, 2023 | 29 | 2023 |
| Human parity on commonsenseqa: Augmenting self-attention with external attention Y Xu, C Zhu, S Wang, S Sun, H Cheng, X Liu, J Gao, P He, M Zeng, ... arXiv preprint arXiv:2112.03254, 2021 | 28 | 2021 |
| Super tickets in pre-trained language models: From model compression to improving generalization C Liang, S Zuo, M Chen, H Jiang, X Liu, P He, T Zhao, W Chen arXiv preprint arXiv:2105.12002, 2021 | 25 | 2021 |
| UnitedQA: A hybrid approach for open domain question answering H Cheng, Y Shen, X Liu, P He, W Chen, J Gao arXiv preprint arXiv:2101.00178, 2021 | 24 | 2021 |
| Check your facts and try again: Improving large language models with external knowledge and automated feedback B Peng, M Galley, P He, H Cheng, Y Xie, Y Hu, Q Huang, L Liden, Z Yu, ... arXiv preprint arXiv:2302.12813, 2023 | 21 | 2023 |
Weizhu ChenMicrosoftVerified email at microsoft.com
