| Survey of hallucination in natural language generation Z Ji, N Lee, R Frieske, T Yu, D Su, Y Xu, E Ishii, Y Bang, A Madotto, ... ACM Computing Surveys, 2022 | 1708* | 2022 |
| NusaCrowd: Open source initiative for Indonesian NLP resources S Cahyawijaya, H Lovenia, AF Aji, G Winata, B Wilie, F Koto, R Mahendra, ... Findings of the Association for Computational Linguistics: ACL 2023, 13745-13818, 2023 | 909 | 2023 |
| A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity Y Bang, S Cahyawijaya, N Lee, W Dai, D Su, B Wilie, H Lovenia, Z Ji, ... AACL, 2023 | 858 | 2023 |
| Gradient descent aligns the layers of deep linear networks Z Ji, M Telgarsky arXiv preprint arXiv:1810.02032, 2018 | 220 | 2018 |
| The implicit bias of gradient descent on nonseparable data Z Ji, M Telgarsky Conference on learning theory, 1772-1798, 2019 | 182 | 2019 |
| Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks Z Ji, M Telgarsky arXiv preprint arXiv:1909.12292, 2019 | 179 | 2019 |
| Risk and parameter convergence of logistic regression Z Ji, M Telgarsky arXiv preprint arXiv:1803.07300, 2018 | 129 | 2018 |
| Crossner: Evaluating cross-domain named entity recognition Z Liu, Y Xu, T Yu, W Dai, Z Ji, S Cahyawijaya, A Madotto, P Fung Proceedings of the AAAI Conference on Artificial Intelligence 35 (15), 13452 …, 2021 | 119 | 2021 |
| Model generalization on COVID-19 fake news detection Y Bang, E Ishii, S Cahyawijaya, Z Ji, P Fung Combating Online Hostile Posts in Regional Languages during Emergency …, 2021 | 56 | 2021 |
| Gradient descent follows the regularization path for general losses Z Ji, M Dudík, RE Schapire, M Telgarsky Conference on Learning Theory, 2109-2136, 2020 | 54 | 2020 |
| Characterizing the implicit bias via a primal-dual analysis Z Ji, M Telgarsky Algorithmic Learning Theory, 772-804, 2021 | 49 | 2021 |
| Neural tangent kernels, transportation mappings, and universal approximation Z Ji, M Telgarsky, R Xian arXiv preprint arXiv:1910.06956, 2019 | 47 | 2019 |
| Multi-hop question generation with graph convolutional network D Su, Y Xu, W Dai, Z Ji, T Yu, P Fung Findings of EMNLP, 2020 | 40 | 2020 |
| Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training W Dai, Z Liu, Z Ji, D Su, P Fung EACL 2023, 2022 | 36 | 2022 |
| Early-stopped neural networks are consistent Z Ji, J Li, M Telgarsky Advances in Neural Information Processing Systems 34, 1805-1817, 2021 | 32 | 2021 |
| Generalization bounds via distillation D Hsu, Z Ji, M Telgarsky, L Wang arXiv preprint arXiv:2104.05641, 2021 | 29 | 2021 |
| Fast margin maximization via dual acceleration Z Ji, N Srebro, M Telgarsky International Conference on Machine Learning, 4860-4869, 2021 | 28 | 2021 |
| Towards Mitigating Hallucination in Large Language Models via Self-Reflection Z Ji, T Yu, Y Xu, N Lee, E Ishii, P Fung Findings of EMNLP 2023, 2023 | 24* | 2023 |
| RHO (): Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding Z Ji, Z Liu, N Lee, T Yu, B Wilie, M Zeng, P Fung Findings of ACL 2023, 2022 | 20* | 2022 |
| A refined primal-dual analysis of the implicit bias Z Ji, M Telgarsky Journal of Environmental Sciences (China) English Ed, 2019 | 18 | 2019 |
Pascale FungDept. of Electronic & Computer Engineering, the Hong Kong University of Science & TechnologyVerified email at ece.ust.hk
