关注
Shengding Hu
Shengding Hu
在 mails.tsinghua.edu.cn 的电子邮件经过验证
标题
引用次数
引用次数
年份
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
54482020
Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification
S Hu, N Ding, H Wang, Z Liu, J Li, M Sun
2912021
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
2442023
Openprompt: An open-source framework for prompt-learning
N Ding, S Hu, W Zhao, Y Chen, Z Liu, HT Zheng, M Sun
arXiv preprint arXiv:2111.01998, 2021
2232021
Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models
N Ding, Y Qin, G Yang, F Wei, Z Yang, Y Su, S Hu, Y Chen, CM Chan, ...
arXiv preprint arXiv:2203.06904, 2022
1702022
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
1622023
Enhancing chat language models by scaling high-quality instructional conversations
N Ding, Y Chen, B Xu, Y Qin, Z Zheng, S Hu, Z Liu, M Sun, B Zhou
arXiv preprint arXiv:2305.14233, 2023
1232023
Prototypical verbalizer for prompt-based few-shot tuning
G Cui, S Hu, N Ding, L Huang, Z Liu
arXiv preprint arXiv:2203.09770, 2022
792022
Graph Policy Network for Transferable Active Learning on Graphs
S Hu, Z Xiong, M Qu, X Yuan, MA Côté, Z Liu, J Tang
NeurIPS'20, 2020
552020
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
arXiv preprint arXiv:2211.04079, 2022
232022
Decoder-only or encoder-decoder? interpreting language model as a regularized encoder-decoder
Z Fu, W Lam, Q Yu, AMC So, S Hu, Z Liu, N Collier
arXiv preprint arXiv:2304.04052, 2023
162023
Sparse structure search for delta tuning
S Hu, Z Zhang, N Ding, Y Wang, Y Wang, Z Liu, M Sun
Advances in Neural Information Processing Systems 35, 9853-9865, 2022
92022
OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models
S Hu, N Ding, W Zhao, X Lv, Z Zhang, Z Liu, M Sun
arXiv preprint arXiv:2307.03084, 2023
82023
Sparse structure search for parameter-efficient tuning
S Hu, Z Zhang, N Ding, Y Wang, Y Wang, Z Liu, M Sun
arXiv preprint arXiv:2206.07382, 2022
82022
Won't Get Fooled Again: Answering Questions with False Premises
S Hu, Y Luo, H Wang, X Cheng, Z Liu, M Sun
arXiv preprint arXiv:2307.02394, 2023
72023
OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems
C He, R Luo, Y Bai, S Hu, ZL Thai, J Shen, J Hu, X Han, Y Huang, ...
arXiv preprint arXiv:2402.14008, 2024
42024
KACC: A multi-task benchmark for knowledge abstraction, concretization and completion
J Zhou, S Hu, X Lv, C Yang, Z Liu, W Xu, J Jiang, J Li, M Sun
arXiv preprint arXiv:2004.13631, 2020
42020
Minicpm: Unveiling the potential of small language models with scalable training strategies
S Hu, Y Tu, X Han, C He, G Cui, X Long, Z Zheng, Y Fang, Y Huang, ...
arXiv preprint arXiv:2404.06395, 2024
32024
Unlock predictable scaling from emergent abilities
S Hu, X Liu, X Han, X Zhang, C He, W Zhao, Y Lin, N Ding, Z Ou, G Zeng, ...
arXiv preprint arXiv:2310.03262, 2023
22023
Arbitrary few parameters are good enough for adapting large-scale pre-trained language models
Y Su, CM Chan, J Cheng, Y Qin, Y Lin, S Hu, Z Yang, N Ding, Z Liu, ...
arXiv preprint arXiv:2306.02320, 2023
22023
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