| A comprehensive capability analysis of gpt-3 and gpt-3.5 series models J Ye, X Chen, N Xu, C Zu, Z Shao, S Liu, Y Cui, Z Zhou, C Gong, Y Shen, ... arXiv preprint arXiv:2303.10420, 2023 | 81 | 2023 |
| How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks X Chen, J Ye, C Zu, N Xu, R Zheng, M Peng, J Zhou, T Gui, Q Zhang, ... arXiv preprint arXiv:2303.00293, 2023 | 25 | 2023 |
| Searching for Optimal Subword Tokenization in Cross-domain NER R Ma, Y Tan, X Zhou, X Chen, D Liang, S Wang, W Wu, T Gui, Q Zhang arXiv preprint arXiv:2206.03352, 2022 | 11 | 2022 |
| Coarse-to-fine Few-shot Learning for Named Entity Recognition R Ma, Z Lin, X Chen, X Zhou, J Wang, T Gui, Q Zhang, X Gao, YW Chen Findings of the Association for Computational Linguistics: ACL 2023, 4115-4129, 2023 | 8 | 2023 |
| Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks X Zhou, R Ma, Y Zou, X Chen, T Gui, Q Zhang, XJ Huang, R Xie, W Wu Proceedings of the 29th International Conference on Computational …, 2022 | 7 | 2022 |
| Learning “O” Helps for Learning More: Handling the Unlabeled Entity Problem for Class-incremental NER R Ma, X Chen, Z Lin, X Zhou, J Wang, T Gui, Q Zhang, X Gao, YW Chen Proceedings of the 61st Annual Meeting of the Association for Computational …, 2023 | 3 | 2023 |
| Learning" O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER R Ma, X Chen, L Zhang, T Gui, Q Zhang, X Huang arXiv preprint arXiv:2210.04676, 2022 | | 2022 |
