| Simple and principled uncertainty estimation with deterministic deep learning via distance awareness JZ Liu, Z Lin, S Padhy, D Tran, T Bedrax-Weiss, B Lakshminarayanan Advances in Neural Information Processing Systems, 2020 | 289 | 2020 |
| Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality WL Chiang, Z Li, Z Lin, Y Sheng, Z Wu, H Zhang, L Zheng, S Zhuang, ... See https://vicuna. lmsys. org (accessed 14 April 2023), 2023 | 214 | 2023 |
| Fast structured decoding for sequence models Z Sun, Z Li, H Wang, D He, Z Lin, Z Deng Advances in Neural Information Processing Systems, 2019 | 103 | 2019 |
| Judging LLM-as-a-judge with MT-Bench and Chatbot Arena L Zheng, WL Chiang, Y Sheng, S Zhuang, Z Wu, Y Zhuang, Z Lin, Z Li, ... arXiv preprint arXiv:2306.05685, 2023 | 67 | 2023 |
| Hint-Based Training for Non-Autoregressive Machine Translation Z Li, Z Lin, D He, F Tian, T Qin, L Wang, TY Liu Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 66 | 2019 |
| Large-scale generative data-free distillation L Luo, M Sandler, Z Lin, A Zhmoginov, A Howard arXiv preprint arXiv:2012.05578, 2020 | 35 | 2020 |
| Pruning redundant mappings in transformer models via spectral-normalized identity prior Z Lin, JZ Liu, Z Yang, N Hua, D Roth Findings of the Association for Computational Linguistics: EMNLP 2020, 2020 | 30 | 2020 |
| A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. JZ Liu, S Padhy, J Ren, Z Lin, Y Wen, G Jerfel, Z Nado, J Snoek, D Tran, ... J. Mach. Learn. Res. 24, 42:1-42:63, 2023 | 18 | 2023 |
| Measuring and improving model-moderator collaboration using uncertainty estimation ID Kivlichan, Z Lin, J Liu, L Vasserman Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021), 36-53, 2021 | 9 | 2021 |
| Parsing meaning representations: Is easier always better? Z Lin, N Xue Proceedings of the first international workshop on designing meaning …, 2019 | 9 | 2019 |
| Implanting rational knowledge into distributed representation at morpheme level Z Lin, Y Liu Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 2954-2961, 2019 | 8 | 2019 |
| Towards a Description of Chinese Morphemic Concepts and Semantic Word-Formation Y Liu, Z Lin, S Kang Journal of Chinese Information Processing 32 (2), 11-20, 2018 | 7* | 2018 |
| Towards collaborative neural-symbolic graph semantic parsing via uncertainty Z Lin, JZ Liu, J Shang Findings of the Association for Computational Linguistics: ACL 2022, 2022 | 6 | 2022 |
| Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification Z Lin, J Liu, J Shang Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2023 | 4 | 2023 |
| Comparing knowledge-intensive and data-intensive models for english resource semantic parsing J Cao, Z Lin, W Sun, X Wan Computational Linguistics 47 (1), 43-68, 2021 | 4* | 2021 |
| Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data Z Lin, Y Duan, Y Zhao, W Sun, X Wan Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 4 | 2018 |
| On Compositional Uncertainty Quantification for Seq2seq Graph Parsing Z Lin, D Phan, P Pasupat, JZ Liu, J Shang The Eleventh International Conference on Learning Representations, 2023 | 1 | 2023 |
Jeremiah Zhe LiuGoogle Research and Harvard UniversityVahvistettu sähköpostiosoite verkkotunnuksessa mail.harvard.edu
