| Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing P Liu, W Yuan, J Fu, Z Jiang, H Hayashi, G Neubig ACM Computing Surveys 55 (9), 1-35, 2023 | 2219 | 2023 |
| How can we know what language models know? Z Jiang, FF Xu, J Araki, G Neubig Transactions of the Association for Computational Linguistics 8, 423-438, 2020 | 876 | 2020 |
| Gsum: A general framework for guided neural abstractive summarization ZY Dou, P Liu, H Hayashi, Z Jiang, G Neubig arXiv preprint arXiv:2010.08014, 2020 | 199 | 2020 |
| How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering Z Jiang, J Araki, H Ding, G Neubig Transactions of the Association for Computational Linguistics 9, 962-977, 2021 | 156 | 2021 |
| Gptscore: Evaluate as you desire J Fu, SK Ng, Z Jiang, P Liu arXiv preprint arXiv:2302.04166, 2023 | 116 | 2023 |
| X-FACTR: Multilingual factual knowledge retrieval from pretrained language models Z Jiang, A Anastasopoulos, J Araki, H Ding, G Neubig arXiv preprint arXiv:2010.06189, 2020 | 91 | 2020 |
| Incorporating external knowledge through pre-training for natural language to code generation FF Xu, Z Jiang, P Yin, B Vasilescu, G Neubig arXiv preprint arXiv:2004.09015, 2020 | 77 | 2020 |
| Graph-revised convolutional network D Yu, R Zhang, Z Jiang, Y Wu, Y Yang Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021 | 76 | 2021 |
| Peer: A collaborative language model T Schick, J Dwivedi-Yu, Z Jiang, F Petroni, P Lewis, G Izacard, Q You, ... arXiv preprint arXiv:2208.11663, 2022 | 56 | 2022 |
| Personalizing search results using hierarchical RNN with query-aware attention S Ge, Z Dou, Z Jiang, JY Nie, JR Wen Proceedings of the 27th ACM international conference on information and …, 2018 | 56 | 2018 |
| Generalizing natural language analysis through span-relation representations Z Jiang, W Xu, J Araki, G Neubig arXiv preprint arXiv:1911.03822, 2019 | 53 | 2019 |
| Automatically mining facets for queries from their search results Z Dou, Z Jiang, S Hu, JR Wen, R Song IEEE Transactions on knowledge and data engineering 28 (2), 385-397, 2015 | 46 | 2015 |
| Learning to diversify search results via subtopic attention Z Jiang, JR Wen, Z Dou, WX Zhao, JY Nie, M Yue Proceedings of the 40th international ACM SIGIR Conference on Research and …, 2017 | 44 | 2017 |
| Docprompting: Generating code by retrieving the docs S Zhou, U Alon, FF Xu, Z Jiang, G Neubig The Eleventh International Conference on Learning Representations, 2022 | 42* | 2022 |
| Active retrieval augmented generation Z Jiang, FF Xu, L Gao, Z Sun, Q Liu, J Dwivedi-Yu, Y Yang, J Callan, ... arXiv preprint arXiv:2305.06983, 2023 | 34 | 2023 |
| OmniTab: Pretraining with natural and synthetic data for few-shot table-based question answering Z Jiang, Y Mao, P He, G Neubig, W Chen arXiv preprint arXiv:2207.03637, 2022 | 26 | 2022 |
| Generating query facets using knowledge bases Z Jiang, Z Dou, JR Wen IEEE transactions on knowledge and data engineering 29 (2), 315-329, 2016 | 23 | 2016 |
| Supervised search result diversification via subtopic attention Z Jiang, Z Dou, WX Zhao, JY Nie, M Yue, JR Wen IEEE Transactions on Knowledge and Data Engineering 30 (10), 1971-1984, 2018 | 14 | 2018 |
| Improving open information extraction via iterative rank-aware learning Z Jiang, P Yin, G Neubig arXiv preprint arXiv:1905.13413, 2019 | 13 | 2019 |
| Retrieval as attention: End-to-end learning of retrieval and reading within a single transformer Z Jiang, L Gao, J Araki, H Ding, Z Wang, J Callan, G Neubig arXiv preprint arXiv:2212.02027, 2022 | 9 | 2022 |
