| Qwen technical report J Bai, S Bai, Y Chu, Z Cui, K Dang, X Deng, Y Fan, W Ge, Y Han, F Huang, ... arXiv preprint arXiv:2309.16609, 2023 | 2696 | 2023 |
| RRHF: Rank responses to align language models with human feedback H Yuan, Z Yuan, C Tan, W Wang, S Huang, F Huang Advances in Neural Information Processing Systems 36, 2024 | 349* | 2024 |
| Structbert: Incorporating language structures into pre-training for deep language understanding W Wang, B Bi, M Yan, C Wu, Z Bao, J Xia, L Peng, L Si arXiv preprint arXiv:1908.04577, 2019 | 336* | 2019 |
| mplug: Effective and efficient vision-language learning by cross-modal skip-connections C Li, H Xu, J Tian, W Wang, M Yan, B Bi, J Ye, H Chen, G Xu, Z Cao, ... arXiv preprint arXiv:2205.12005, 2022 | 228* | 2022 |
| Multi-granularity hierarchical attention fusion networks for reading comprehension and question answering W Wang, M Yan, C Wu arXiv preprint arXiv:1811.11934, 2018 | 215 | 2018 |
| mplug-2: A modularized multi-modal foundation model across text, image and video H Xu, Q Ye, M Yan, Y Shi, J Ye, Y Xu, C Li, B Bi, Q Qian, W Wang, G Xu, ... International Conference on Machine Learning, 38728-38748, 2023 | 168* | 2023 |
| StructuralLM: Structural pre-training for form understanding C Li, B Bi, M Yan, W Wang, S Huang, F Huang, L Si arXiv preprint arXiv:2105.11210, 2021 | 137 | 2021 |
| How well do large language models perform in arithmetic tasks? Z Yuan, H Yuan, C Tan, W Wang, S Huang arXiv preprint arXiv:2304.02015, 2023 | 108 | 2023 |
| How abilities in large language models are affected by supervised fine-tuning data composition G Dong, H Yuan, K Lu, C Li, M Xue, D Liu, W Wang, Z Yuan, C Zhou, ... arXiv preprint arXiv:2310.05492, 2023 | 100 | 2023 |
| Palm: Pre-training an autoencoding&autoregressive language model for context-conditioned generation B Bi, C Li, C Wu, M Yan, W Wang, S Huang, F Huang, L Si arXiv preprint arXiv:2004.07159, 2020 | 81 | 2020 |
| VECO: Variable and flexible cross-lingual pre-training for language understanding and generation F Luo, W Wang, J Liu, Y Liu, B Bi, S Huang, F Huang, L Si arXiv preprint arXiv:2010.16046, 2020 | 70* | 2020 |
| A deep cascade model for multi-document reading comprehension M Yan, J Xia, C Wu, B Bi, Z Zhao, J Zhang, L Si, R Wang, W Wang, ... Proceedings of the AAAI conference on artificial intelligence 33 (01), 7354-7361, 2019 | 67* | 2019 |
| Incorporating external knowledge into machine reading for generative question answering B Bi, C Wu, M Yan, W Wang, J Xia, C Li arXiv preprint arXiv:1909.02745, 2019 | 52 | 2019 |
| IDST at TREC 2019 Deep Learning Track: Deep Cascade Ranking with Generation-based Document Expansion and Pre-trained Language Modeling. M Yan, C Li, C Wu, B Bi, W Wang, J Xia, L Si TREC, 2019 | 41* | 2019 |
| SemVLP: Vision-language pre-training by aligning semantics at multiple levels C Li, M Yan, H Xu, F Luo, W Wang, B Bi, S Huang arXiv preprint arXiv:2103.07829, 2021 | 35* | 2021 |
| Addressing semantic drift in generative question answering with auxiliary extraction C Li, B Bi, M Yan, W Wang, S Huang Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 24 | 2021 |
| A unified pretraining framework for passage ranking and expansion M Yan, C Li, B Bi, W Wang, S Huang Proceedings of the AAAI Conference on Artificial Intelligence 35 (5), 4555-4563, 2021 | 21 | 2021 |
| Stronghold: fast and affordable billion-scale deep learning model training X Sun, W Wang, S Qiu, R Yang, S Huang, J Xu, Z Wang SC22: International Conference for High Performance Computing, Networking …, 2022 | 18 | 2022 |
| Grid-vlp: Revisiting grid features for vision-language pre-training M Yan, H Xu, C Li, B Bi, J Tian, M Gui, W Wang arXiv preprint arXiv:2108.09479, 2021 | 11 | 2021 |
| Achieving Human Parity on Visual Question Answering M Yan, H Xu, C Li, J Tian, B Bi, W Wang, X Xu, J Zhang, S Huang, ... ACM Transactions on Information Systems 41 (3), 1-40, 2023 | 10 | 2023 |
Songfang Huang（黄松芳）Peking University, Alibaba DAMO, IBM Research, The University of EdinburghVerified email at pku.edu.cn
