| Instruction tuning with gpt-4 B Peng, C Li, P He, M Galley, J Gao arXiv preprint arXiv:2304.03277, 2023 | 931 | 2023 |
| An introduction to computational networks and the computational network toolkit D Yu, A Eversole, M Seltzer, K Yao, Z Huang, B Guenter, O Kuchaiev, ... Microsoft Technical Report MSR-TR-2014–112, 2014 | 473 | 2014 |
| 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 | 461 | 2023 |
| Chameleon: Plug-and-play compositional reasoning with large language models P Lu, B Peng, H Cheng, M Galley, KW Chang, YN Wu, SC Zhu, J Gao NeurIPS 2023, 2023 | 421 | 2023 |
| Spoken language understanding using long short-term memory neural networks K Yao, B Peng, Y Zhang, D Yu, G Zweig, Y Shi 2014 IEEE spoken language technology workshop (SLT), 189-194, 2014 | 421 | 2014 |
| Soloist: Building Task Bots at Scale with Transfer Learning and Machine Teaching B Peng, C Li, J Li, S Shayandeh, L Liden, J Gao Transactions of the Association for Computational Linguistics 9, 807-824, 2021 | 347* | 2021 |
| Task-oriented dialogue system for automatic diagnosis Z Wei, Q Liu, B Peng, H Tou, T Chen, XJ Huang, KF Wong, X Dai Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 249 | 2018 |
| Deep dyna-q: Integrating planning for task-completion dialogue policy learning B Peng, X Li, J Gao, J Liu, KF Wong ACL 2018, 2018 | 246 | 2018 |
| Composite task-completion dialogue policy learning via hierarchical deep reinforcement learning B Peng, X Li, L Li, J Gao, A Celikyilmaz, S Lee, KF Wong EMNLP 2017, 2017 | 242 | 2017 |
| Few-shot natural language generation for task-oriented dialog B Peng, C Zhu, C Li, X Li, J Li, M Zeng, J Gao EMNLP 2020, 2020 | 236 | 2020 |
| Optimus: Organizing sentences via pre-trained modeling of a latent space C Li, X Gao, Y Li, B Peng, X Li, Y Zhang, J Gao EMNLP 2020, 2020 | 193 | 2020 |
| Recurrent conditional random field for language understanding K Yao, B Peng, G Zweig, D Yu, X Li, F Gao 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 170 | 2014 |
| Attention with intention for a neural network conversation model K Yao, G Zweig, B Peng arXiv preprint arXiv:1510.08565, 2015 | 149 | 2015 |
| Convlab-2: An open-source toolkit for building, evaluating, and diagnosing dialogue systems Q Zhu, Z Zhang, Y Fang, X Li, R Takanobu, J Li, B Peng, J Gao, X Zhu, ... arXiv preprint arXiv:2002.04793, 2020 | 134 | 2020 |
| Explanations from large language models make small reasoners better S Li, J Chen, Y Shen, Z Chen, X Zhang, Z Li, H Wang, J Qian, B Peng, ... arXiv preprint arXiv:2210.06726, 2022 | 132 | 2022 |
| Convlab: Multi-domain end-to-end dialog system platform S Lee, Q Zhu, R Takanobu, X Li, Y Zhang, Z Zhang, J Li, B Peng, X Li, ... arXiv preprint arXiv:1904.08637, 2019 | 115 | 2019 |
| Few-shot named entity recognition: An empirical baseline study J Huang, C Li, K Subudhi, D Jose, S Balakrishnan, W Chen, B Peng, ... Proceedings of the 2021 conference on empirical methods in natural language …, 2021 | 109 | 2021 |
| Guiding Large Language Models via Directional Stimulus Prompting Z Li, B Peng, P He, M Galley, J Gao, X Yan NeurIPS 2023, 2023 | 106 | 2023 |
| Recurrent neural networks with external memory for spoken language understanding B Peng, K Yao, L Jing, KF Wong Natural Language Processing and Chinese Computing: 4th CCF Conference, NLPCC …, 2015 | 98 | 2015 |
| Few-shot named entity recognition: A comprehensive study J Huang, C Li, K Subudhi, D Jose, S Balakrishnan, W Chen, B Peng, ... arXiv preprint arXiv:2012.14978, 2020 | 95 | 2020 |
Jianfeng GaoMicrosoft Research, RedmondE-mailová adresa ověřena na: microsoft.com
