Graph neural networks: A review of methods and applications J Zhou, G Cui, S Hu, Z Zhang, C Yang, Z Liu, L Wang, C Li, M Sun AI open 1, 57-81, 2020 | 5440 | 2020 |
Adaptive graph encoder for attributed graph embedding G Cui, J Zhou, C Yang, Z Liu KDD 2020, 976-985, 2020 | 194 | 2020 |
Tool learning with foundation models Y Qin, S Hu, Y Lin, W Chen, N Ding, G Cui, Z Zeng, Y Huang, C Xiao, ... arXiv preprint arXiv:2304.08354, 2023 | 160 | 2023 |
Introduction to graph neural networks Z Liu, J Zhou Springer Nature, 2022 | 140 | 2022 |
Full-scale information diffusion prediction with reinforced recurrent networks C Yang, H Wang, J Tang, C Shi, M Sun, G Cui, Z Liu IEEE Transactions on Neural Networks and Learning Systems 34 (5), 2271-2283, 2021 | 106 | 2021 |
Prototypical verbalizer for prompt-based few-shot tuning G Cui, S Hu, N Ding, L Huang, Z Liu ACL 2022, 2022 | 79 | 2022 |
Ultrafeedback: Boosting language models with high-quality feedback G Cui, L Yuan, N Ding, G Yao, W Zhu, Y Ni, G Xie, Z Liu, M Sun ICML 2024, 2023 | 59 | 2023 |
Exploring the universal vulnerability of prompt-based learning paradigm L Xu, Y Chen, G Cui, H Gao, Z Liu NAACL 2022 Findings, 2022 | 54 | 2022 |
A unified evaluation of textual backdoor learning: Frameworks and benchmarks G Cui, L Yuan, B He, Y Chen, Z Liu, M Sun NeurIPS 2022 Datasets and Benchmarks Track, 2022 | 42 | 2022 |
A close look into the calibration of pre-trained language models Y Chen, L Yuan, G Cui, Z Liu, H Ji ACL 2023, 2022 | 34 | 2022 |
Why should adversarial perturbations be imperceptible? rethink the research paradigm in adversarial NLP Y Chen, H Gao, G Cui, F Qi, L Huang, Z Liu, M Sun EMNLP 2022, 2022 | 22 | 2022 |
Revisiting Out-of-distribution Robustness in NLP: Benchmarks, Analysis, and LLMs Evaluations L Yuan, Y Chen, G Cui, H Gao, F Zou, X Cheng, H Ji, Z Liu, M Sun NeurIPS 2023 Datasets and Benchmarks Track 36, 2024 | 21 | 2024 |
Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback T Yu, Y Yao, H Zhang, T He, Y Han, G Cui, J Hu, Z Liu, HT Zheng, M Sun, ... arXiv preprint arXiv:2312.00849, 2023 | 14 | 2023 |
Moderate-fitting as a natural backdoor defender for pre-trained language models B Zhu, Y Qin, G Cui, Y Chen, W Zhao, C Fu, Y Deng, Z Liu, J Wang, W Wu, ... Advances in Neural Information Processing Systems 35, 1086-1099, 2022 | 12 | 2022 |
Machine-learning-driven matrix ordering for power grid analysis G Cui, W Yu, X Li, Z Zeng, B Gu 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE), 984-987, 2019 | 12 | 2019 |
INTERVENOR: Prompt the Coding Ability of Large Language Models with the Interactive Chain of Repairing H Wang, Z Liu, S Wang, G Cui, N Ding, Z Liu, G Yu arXiv preprint arXiv:2311.09868, 2023 | 6 | 2023 |
Few-shot classification with hypersphere modeling of prototypes N Ding, Y Chen, G Cui, X Wang, HT Zheng, Z Liu, P Xie arXiv preprint arXiv:2211.05319, 2022 | 6 | 2022 |
From adversarial arms race to model-centric evaluation: Motivating a unified automatic robustness evaluation framework Y Chen, H Gao, G Cui, L Yuan, D Kong, H Wu, N Shi, B Yuan, L Huang, ... ACL 2023 Findings, 2023 | 4 | 2023 |
Decoder Tuning: Efficient Language Understanding as Decoding G Cui, W Li, N Ding, L Huang, Z Liu, M Sun ACL 2023, 2022 | 4 | 2022 |
Evaluating modules in graph contrastive learning G Cui, Y Du, C Yang, J Zhou, L Xu, X Zhou, X Cheng, Z Liu arXiv preprint arXiv:2106.08171, 2021 | 3 | 2021 |