| Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT Z Wu, Y Chen, B Kao, Q Liu arXiv preprint arXiv:2004.14786, 2020 | 131 | 2020 |
| A Survey for In-context Learning Q Dong, L Li, D Dai, C Zheng, Z Wu, B Chang, X Sun, J Xu, Z Sui arXiv preprint arXiv:2301.00234, 2022 | 120 | 2022 |
| Next: a neural network framework for next poi recommendation Z Zhang, C Li, Z Wu, A Sun, D Ye, X Luo Frontiers of Computer Science 14, 314-333, 2020 | 81 | 2020 |
| DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models S Gong, M Li, J Feng, Z Wu, LP Kong arXiv preprint arXiv:2210.08933, 2022 | 77 | 2022 |
| ZeroGen: Efficient Zero-shot Learning via Dataset Generation J Ye, J Gao, Q Li, H Xu, J Feng, Z Wu, T Yu, L Kong arXiv preprint arXiv:2202.07922, 2022 | 40 | 2022 |
| 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 Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 36* | 2021 |
| Towards practical open knowledge base canonicalization TH Wu, Z Wu, B Kao, P Yin Proceedings of the 27th ACM International Conference on Information and …, 2018 | 25 | 2018 |
| PERQ: Predicting, Explaining, and Rectifying Failed Questions in KB-QA Systems Z Wu, B Kao, TH Wu, P Yin, Q Liu ACM International Conference on Web Search and Data Mining (WSDM), 2020 | 15 | 2020 |
| Self-Adaptive In-Context Learning: An Information Compression Perspective for In-Context Example Selection and Ordering Z Wu, Y Wang, J Ye, L Kong | 12* | |
| Compositional Exemplars for In-context Learning J Ye, Z Wu, J Feng, T Yu, L Kong arXiv preprint arXiv:2302.05698, 2023 | 10 | 2023 |
| ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback J Ye, J Gao, J Feng, Z Wu, T Yu, L Kong arXiv preprint arXiv:2210.12329, 2022 | 10 | 2022 |
| Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning J Gao, R Pi, LIN Yong, H Xu, J Ye, Z Wu, W ZHANG, X Liang, Z Li, L Kong The Eleventh International Conference on Learning Representations, 2022 | 10* | 2022 |
| COLO: A Contrastive Learning based Re-ranking Framework for One-Stage Summarization C An, M Zhong, Z Wu, Q Zhu, X Huang, X Qiu arXiv preprint arXiv:2209.14569, 2022 | 9 | 2022 |
| OpenICL: An Open-Source Framework for In-context Learning Z Wu, YX Wang, J Ye, J Feng, J Xu, Y Qiao, Z Wu arXiv preprint arXiv:2303.02913, 2023 | 7 | 2023 |
| In-Context Learning with Many Demonstration Examples M Li, S Gong, J Feng, Y Xu, J Zhang, Z Wu, L Kong arXiv preprint arXiv:2302.04931, 2023 | 5 | 2023 |
| Lexical Knowledge Internalization for Neural Dialog Generation Z Wu, W Bi, X Li, L Kong, B Kao arXiv preprint arXiv:2205.01941, 2022 | 2 | 2022 |
| Cascaded Head-colliding Attention L Zheng, Z Wu, L Kong arXiv preprint arXiv:2105.14850, 2021 | 2 | 2021 |
| Unsupervised Explanation Generation via Correct Instantiations S Cheng, Z Wu, J Chen, Z Li, Y Liu, L Kong Proceedings of the AAAI Conference on Artificial Intelligence 37 (11), 12700 …, 2023 | 1 | 2023 |
| Can We Edit Factual Knowledge by In-Context Learning? C Zheng, L Li, Q Dong, Y Fan, Z Wu, J Xu, B Chang arXiv preprint arXiv:2305.12740, 2023 | 1 | 2023 |
| Explanation Regeneration via Information Bottleneck Q Li, Z Wu, L Kong, W Bi arXiv preprint arXiv:2212.09603, 2022 | 1 | 2022 |
Lingpeng KongGoogle DeepMind, The University of Hong KongПотвърден имейл адрес: cs.hku.hk
