| Lora: Low-rank adaptation of large language models EJ Hu, Y Shen, P Wallis, Z Allen-Zhu, Y Li, S Wang, L Wang, W Chen arXiv preprint arXiv:2106.09685, 2021 | 10043 | 2021 |
| Deberta: Decoding-enhanced bert with disentangled attention P He, X Liu, J Gao, W Chen arXiv preprint arXiv:2006.03654, 2020 | 2878 | 2020 |
| On the variance of the adaptive learning rate and beyond L Liu, H Jiang, P He, W Chen, X Liu, J Gao, J Han arXiv preprint arXiv:1908.03265, 2019 | 2439 | 2019 |
| Multi-task deep neural networks for natural language understanding X Liu, P He, W Chen, J Gao arXiv preprint arXiv:1901.11504, 2019 | 1506 | 2019 |
| What Makes Good In-Context Examples for GPT-? J Liu, D Shen, Y Zhang, B Dolan, L Carin, W Chen arXiv preprint arXiv:2101.06804, 2021 | 1255 | 2021 |
| Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing P He, J Gao, W Chen arXiv preprint arXiv:2111.09543, 2021 | 1008 | 2021 |
| Phi-3 technical report: A highly capable language model locally on your phone M Abdin, J Aneja, H Awadalla, A Awadallah, AA Awan, N Bach, A Bahree, ... arXiv preprint arXiv:2404.14219, 2024 | 656 | 2024 |
| 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 arXiv preprint arXiv:1911.03437, 2019 | 506 | 2019 |
| AdaLoRA: Adaptive budget allocation for parameter-efficient fine-tuning Q Zhang, M Chen, A Bukharin, N Karampatziakis, P He, Y Cheng, ... arXiv preprint arXiv:2303.10512, 2023 | 417 | 2023 |
| 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 | 408 | 2023 |
| Reasonet: Learning to stop reading in machine comprehension Y Shen, PS Huang, J Gao, W Chen Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017 | 339 | 2017 |
| Agieval: A human-centric benchmark for evaluating foundation models W Zhong, R Cui, Y Guo, Y Liang, S Lu, Y Wang, A Saied, W Chen, ... arXiv preprint arXiv:2304.06364, 2023 | 326 | 2023 |
| Understanding the difficulty of training transformers L Liu, X Liu, J Gao, W Chen, J Han arXiv preprint arXiv:2004.08249, 2020 | 294 | 2020 |
| Short text conceptualization using a probabilistic knowledgebase Y Song, H Wang, Z Wang, H Li, W Chen Proceedings of the twenty-second international joint conference on …, 2011 | 294 | 2011 |
| Codet: Code generation with generated tests B Chen, F Zhang, A Nguyen, D Zan, Z Lin, JG Lou, W Chen arXiv preprint arXiv:2207.10397, 2022 | 293 | 2022 |
| On the advance of making language models better reasoners Y Li, Z Lin, S Zhang, Q Fu, B Chen, JG Lou, W Chen arXiv preprint arXiv:2206.02336, 2022 | 282* | 2022 |
| Diffusion-gan: Training gans with diffusion Z Wang, H Zheng, P He, W Chen, M Zhou arXiv preprint arXiv:2206.02262, 2022 | 238 | 2022 |
| 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 | 237 | 2020 |
| 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 | 223 | 2019 |
| Tuning large neural networks via zero-shot hyperparameter transfer G Yang, E Hu, I Babuschkin, S Sidor, X Liu, D Farhi, N Ryder, J Pachocki, ... Advances in Neural Information Processing Systems 34, 17084-17097, 2021 | 220* | 2021 |
