Shen, Sheng
University of California, Berkeley
11 papers on CoChatORCID 0000-0003-4734-1008
Most cited papers
- Verified
Multitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · arXiv
Topic ModelingDomain Adaptation and Few-Shot Learning
Cited by 612
- Verified
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT
Shen, S., Dong, Z., Ye, J., Ma, L., Yao, Z., Gholami, A., Mahoney, M. W., & Keutzer, K. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence
Advanced Neural Network ApplicationsTopic Modeling
Cited by 437
- Verified
Crosslingual Generalization through Multitask Finetuning
Muennighoff, N., Wang, T., Sutawika, L., Roberts, A., Biderman, S., Le Scao, T., Bari, M. S., Shen, S., Yong, Z. X., Schoelkopf, H., et al. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Topic ModelingDomain Adaptation and Few-Shot Learning
Cited by 361
- Verified
RAFT: Adapting Language Model to Domain Specific RAG
Zhang, T., Patil, S. G., Jain, N., Shen, S., Zaharia, M., Stoica, I., & Gonzalez, J. E. (2024) · arXiv
Topic ModelingInformation Retrieval and Search Behavior
Cited by 286
- Verified
AgentBench: Evaluating LLMs as Agents
Liu, X., Yu, H., Zhang, H., Xu, Y., Lei, X., Lai, H., Gu, Y., Ding, H., Men, K., Yang, K., et al. (2023) · arXiv
Natural Language Processing TechniquesTopic Modeling
Cited by 79
- Verified
Poisoning Language Models During Instruction Tuning
Wan, A., Wallace, E., Shen, S., & Klein, D. (2023) · arXiv
Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data
Cited by 42
- Verified
What Language Model to Train if You Have One Million GPU Hours?
Le Scao, T., Wang, T., Hesslow, D., Bekman, S., Bari, M. S., Biderman, S., Elsahar, H., Muennighoff, N., Phang, J., Press, O., et al. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022
Topic ModelingNatural Language Processing Techniques
Cited by 33
- Verified
Crosslingual Generalization through Multitask Finetuning
Muennighoff, N., Wang, T., Sutawika, L., Roberts, A., Biderman, S., Scao, T. L., Bari, M. S., Shen, S., Yong, Z.-X., Schoelkopf, H., et al. (2022) · arXiv
Topic ModelingDomain Adaptation and Few-Shot Learning
Cited by 29
- Verified
On the Generation of Medical Question-Answer Pairs
Shen, S., Li, Y., Du, N., Wu, X., Xie, Y., Ge, S., Yang, T., Wang, K., Liang, X., & Fan, W. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence
Topic ModelingNatural Language Processing Techniques
Cited by 25
- Verified
What Language Model to Train if You Have One Million GPU Hours?
Scao, T. L., Wang, T., Hesslow, D., Saulnier, L., Bekman, S., Bari, M. S., Biderman, S., Elsahar, H., Muennighoff, N., Phang, J., et al. (2022) · arXiv
Topic ModelingNatural Language Processing Techniques
Cited by 9
- Verified
AvalonBench: Evaluating LLMs Playing the Game of Avalon
Light, J., Cai, M., Shen, S., & Hu, Z. (2023) · arXiv
Artificial Intelligence in GamesArtificial Intelligence Applications
Cited by 1
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