| Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models Y Zhang, Y Li, L Cui, D Cai, L Liu, T Fu, X Huang, E Zhao, Y Zhang, ... | 970 | 2023 |
| Event extraction as machine reading comprehension J Liu, Y Chen, K Liu, W Bi, X Liu Proceedings of the 2020 conference on empirical methods in natural language …, 2020 | 354 | 2020 |
| Multi-label classification on tree-and dag-structured hierarchies W Bi, JT Kwok Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011 | 255 | 2011 |
| Efficient Multi-label Classification with Many Labels W Bi, JT Kwok Proceedings of the 30th International Conference on Machine Learning (ICML …, 2013 | 248 | 2013 |
| Efficient Training of Visual Transformers with Small-Size Datasets Y Liu, E Sangineto, W Bi, N Sebe, B Lepri, M De Nadai NeurIPS 2021, 2021 | 235* | 2021 |
| Large-scale Nyström kernel matrix approximation using randomized SVD M Li, W Bi, JT Kwok, BL Lu IEEE transactions on neural networks and learning systems 26 (1), 152-164, 2014 | 145 | 2014 |
| Multilabel Classification with Label Correlations and Missing Labels W Bi, JT Kwok Twenty-Eighth Conference on Artificial Intelligence, 2014 | 107 | 2014 |
| Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memory D Cai, Y Wang, V Bi, Z Tu, X Liu, W Lam, S Shi NAACL2019, 2018 | 95 | 2018 |
| Tablegpt: Few-shot table-to-text generation with table structure reconstruction and content matching H Gong, Y Sun, X Feng, B Qin, W Bi, X Liu, T Liu Proceedings of the 28th International Conference on Computational …, 2020 | 93 | 2020 |
| Retrieval-guided dialogue response generation via a matching-to-generation framework D Cai, Y Wang, W Bi, Z Tu, X Liu, S Shi Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 89 | 2019 |
| Good for Misconceived Reasons: An Empirical Revisiting on the Need for Visual Context in Multimodal Machine Translation Z Wu, L Kong, W Bi, X Li, B Kao ACL2021, 2021 | 86* | 2021 |
| Mage: Machine-generated text detection in the wild Y Li, Q Li, L Cui, W Bi, Z Wang, L Wang, L Yang, S Shi, Y Zhang ACL2024, 2024 | 84* | 2024 |
| A batch normalized inference network keeps the kl vanishing away Q Zhu, J Su, W Bi, X Liu, X Ma, X Li, D Wu ACL2020, 2020 | 79 | 2020 |
| Learning to Predict from Crowdsourced Data W Bi, L Wang, JT Kwok, Z Tu Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014 | 75 | 2014 |
| Knowledge fusion of large language models F Wan, X Huang, D Cai, X Quan, W Bi, S Shi ICLR2024, 2024 | 69 | 2024 |
| Improving knowledge-aware dialogue generation via knowledge base question answering J Wang, J Liu, W Bi, X Liu, K He, R Xu, M Yang Proceedings of the AAAI conference on artificial intelligence 34 (05), 9169-9176, 2020 | 66 | 2020 |
| Generating multiple diverse responses for short-text conversation J Gao, W Bi, X Liu, J Li, S Shi Proceedings of the AAAI Conference on Artificial Intelligence 33, 6383-6390, 2019 | 63 | 2019 |
| Bayes-optimal hierarchical multilabel classification W Bi, TJ Kwok TKDE, 2015 | 56 | 2015 |
| A Discrete CVAE for Response Generation on Short-Text Conversation J Gao, W Bi, X Liu, J Li, G Zhou, S Shi EMNLP2019, 2019 | 54 | 2019 |
| Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References L Qiu, J Li, W Bi, D Zhao, R Yan ACL2019, 2019 | 54 | 2019 |
Shuming ShiTencent AI LabVerified email at tencent.com
