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 | 8872 | 2021 |
Deberta: Decoding-enhanced bert with disentangled attention P He, X Liu, J Gao, W Chen arXiv preprint arXiv:2006.03654, 2020 | 2714 | 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 | 2373 | 2019 |
Multi-task deep neural networks for natural language understanding X Liu, P He, W Chen, J Gao arXiv preprint arXiv:1901.11504, 2019 | 1474 | 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 | 1182 | 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 | 931 | 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 | 509 | 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 | 497 | 2019 |
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 | 384 | 2023 |
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 | 379 | 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 | 338 | 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 | 307 | 2023 |
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 | 293 | 2011 |
Understanding the difficulty of training transformers L Liu, X Liu, J Gao, W Chen, J Han arXiv preprint arXiv:2004.08249, 2020 | 288 | 2020 |
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 | 271* | 2022 |
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 | 267 | 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 | 230 | 2020 |
Diffusion-gan: Training gans with diffusion Z Wang, H Zheng, P He, W Chen, M Zhou arXiv preprint arXiv:2206.02262, 2022 | 222 | 2022 |
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 | 217 | 2019 |
TAPEX: Table pre-training via learning a neural SQL executor Q Liu, B Chen, J Guo, M Ziyadi, Z Lin, W Chen, JG Lou arXiv preprint arXiv:2107.07653, 2021 | 212 | 2021 |