| Domain-specific language model pretraining for biomedical natural language processing Y Gu*, R Tinn*, H Cheng*, M Lucas, N Usuyama, X Liu, T Naumann, ... ACM Transactions on Computing for Healthcare (HEALTH) 3 (1), 1-23, 2021 | 1667 | 2021 |
| Language models for image captioning: The quirks and what works J Devlin, H Cheng, H Fang, S Gupta, L Deng, X He, G Zweig, M Mitchell ACL, 2015 | 338 | 2015 |
| 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 | 300 | 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 | 263 | 2023 |
| 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 | 177 | 2020 |
| Mathvista: Evaluating math reasoning in visual contexts with gpt-4v, bard, and other large multimodal models P Lu, H Bansal, T Xia, J Liu, C Li, H Hajishirzi, H Cheng, KW Chang, ... ICLR, 2024 | 148* | 2024 |
| Dialogue State Tracking with a Language Model using Schema-Driven Prompting CH Lee, H Cheng, M Ostendorf EMNLP, 2021 | 109 | 2021 |
| Augmenting Language Models with Long-Term Memory W Wang, L Dong, H Cheng, X Liu, X Yan, J Gao, F Wei NeurIPS, 2023 | 93 | 2023 |
| Fine-tuning large neural language models for biomedical natural language processing R Tinn*, H Cheng*, Y Gu, N Usuyama, X Liu, T Naumann, J Gao, H Poon Patterns 4 (4), 2023 | 83 | 2023 |
| Sounding Board: A User-Centric and Content-Driven Social Chatbot H Fang, H Cheng, M Sap, E Clark, A Holtzman, Y Choi, NA Smith, ... NAACL (demo), 2018 | 75 | 2018 |
| 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 Competition and Demonstration Track, 86-111, 2021 | 70 | 2021 |
| Bi-directional Attention with Agreement for Dependency Parsing H Cheng, H Fang, X He, J Gao, L Deng EMNLP, 2016 | 59 | 2016 |
| Llava-plus: Learning to use tools for creating multimodal agents S Liu, H Cheng, H Liu, H Zhang, F Li, T Ren, X Zou, J Yang, H Su, J Zhu, ... ECCV, 2024 | 56 | 2024 |
| 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, ... ACL (demo), 2020 | 54 | 2020 |
| 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, ... IJCAI, 2022 | 53 | 2022 |
| Open-Domain Name Error Detection using a Multi-Task RNN H Cheng, H Fang, M Ostendorf EMNLP, 2015 | 48 | 2015 |
| A survey of knowledge-intensive nlp with pre-trained language models D Yin, L Dong, H Cheng, X Liu, KW Chang, F Wei, J Gao arXiv preprint arXiv:2202.08772, 2022 | 47 | 2022 |
| Open Domain Question Answering with A Unified Knowledge Interface K Ma, H Cheng, X Liu, E Nyberg, J Gao ACL, 2022 | 44* | 2022 |
| UnitedQA: A Hybrid Approach for Open Domain Question Answering H Cheng, Y Shen, X Liu, P He, W Chen, J Gao ACL, 2021 | 44 | 2021 |
| Sounding board–university of washington’s alexa prize submission H Fang, H Cheng, E Clark, A Holtzman, M Sap, M Ostendorf, Y Choi, ... Alexa prize proceedings, 2017 | 43 | 2017 |
Jianfeng GaoMicrosoft Research, Redmond在 microsoft.com 的电子邮件经过验证
