Parameter-efficient fine-tuning of large-scale pre-trained language models N Ding*, Y Qin*, G Yang, F Wei, Z Yang, Y Su, S Hu, Y Chen, CM Chan, ... Nature Machine Intelligence 5 (3), 220-235, 2023 | 776* | 2023 |
KEPLER: A unified model for knowledge embedding and pre-trained language representation X Wang, T Gao, Z Zhu, Z Zhang, Z Liu, J Li, J Tang Transactions of the Association for Computational Linguistics 9, 176-194, 2021 | 721 | 2021 |
MAVEN: A Massive General Domain Event Detection Dataset X Wang, Z Wang, X Han, W Jiang, R Han, Z Liu, J Li, P Li, Y Lin, J Zhou Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 193 | 2020 |
Adversarial training for weakly supervised event detection X Wang*, X Han*, Z Liu, M Sun, P Li Proceedings of the 2019 Conference of the North American Chapter of the …, 2019 | 173 | 2019 |
On transferability of prompt tuning for natural language processing Y Su*, X Wang*, Y Qin, CM Chan, Y Lin, H Wang, K Wen, Z Liu, P Li, J Li, ... Proceedings of the 2022 Conference of the North American Chapter of the …, 2022 | 143 | 2022 |
CPM: A large-scale generative Chinese pre-trained language model Z Zhang, X Han, H Zhou, P Ke, Y Gu, D Ye, Y Qin, Y Su, H Ji, J Guan, F Qi, ... AI Open 2, 93-99, 2021 | 117 | 2021 |
CLEVE: Contrastive pre-training for event extraction Z Wang*, X Wang*, X Han, Y Lin, L Hou, Z Liu, P Li, J Li, J Zhou Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 113 | 2021 |
HMEAE: Hierarchical modular event argument extraction X Wang*, Z Wang*, X Han, Z Liu, J Li, P Li, M Sun, J Zhou, X Ren Proceedings of the 2019 Conference on empirical methods in natural language …, 2019 | 105 | 2019 |
Benchmarking Foundation Models with Language-Model-as-an-Examiner Y Bai*, J Ying*, Y Cao, X Lv, Y He, X Wang, J Yu, K Zeng, Y Xiao, H Lyu, ... Advances in Neural Information Processing Systems 36, 78142--78167, 2023 | 102 | 2023 |
KoLA: Carefully Benchmarking World Knowledge of Large Language Models J Yu*, X Wang*, S Tu, S Cao, D Zhang-Li, X Lv, H Peng, Z Yao, X Zhang, ... arXiv preprint arXiv:2306.09296, 2023 | 102 | 2023 |
Finding Skill Neurons in Pre-trained Transformer-based Language Models X Wang*, K Wen*, Z Zhang, L Hou, Z Liu, J Li Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 74 | 2022 |
Train No Evil: Selective Masking for Task-guided Pre-training Y Gu, Z Zhang, X Wang, Z Liu, M Sun Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 68 | 2020 |
LEVEN: A Large-Scale Chinese Legal Event Detection Dataset F Yao*, C Xiao*, X Wang, Z Liu, L Hou, C Tu, J Li, Y Liu, W Shen, M Sun Findings of the Association for Computational Linguistics: ACL 2022, 183-201, 2022 | 63 | 2022 |
Exploring Universal Intrinsic Task Subspace for Few-Shot Learning via Prompt Tuning Y Qin*, X Wang*, Y Su, Y Lin, N Ding, J Yi, W Chen, Z Liu, J Li, L Hou, P Li, ... IEEE/ACM Transactions on Audio, Speech, and Language Processing 32, 3631-3643, 2024 | 58* | 2024 |
Adversarial multi-lingual neural relation extraction X Wang*, X Han*, Y Lin, Z Liu, M Sun Proceedings of the 27th International Conference on Computational …, 2018 | 48 | 2018 |
MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction X Wang*, Y Chen*, N Ding, H Peng, Z Wang, Y Lin, X Han, L Hou, J Li, ... Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 41 | 2022 |
COPEN: Probing Conceptual Knowledge in Pre-trained Language Models H Peng*, X Wang*, S Hu, H Jin, L Hou, J Li, Z Liu, Q Liu Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 39 | 2022 |
Emergent Modularity in Pre-trained Transformers Z Zhang*, Z Zeng*, Y Lin, C Xiao, X Wang, X Han, Z Liu, R Xie, M Sun, ... Findings of the Association for Computational Linguistics: ACL 2023, 4066--4083, 2023 | 21 | 2023 |
Sub-character tokenization for chinese pretrained language models C Si*, Z Zhang*, Y Chen*, F Qi, X Wang, Z Liu, Y Wang, Q Liu, M Sun Transactions of the Association for Computational Linguistics 11, 469-487, 2023 | 20* | 2023 |
When does in-context learning fall short and why? a study on specification-heavy tasks H Peng, X Wang, J Chen, W Li, Y Qi, Z Wang, Z Wu, K Zeng, B Xu, L Hou, ... arXiv preprint arXiv:2311.08993, 2023 | 19 | 2023 |