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Percy Liang
Percy Liang
Associate Professor of Computer Science, Stanford University
Verified email at cs.stanford.edu - Homepage
Title
Cited by
Cited by
Year
Squad: 100,000+ questions for machine comprehension of text
P Rajpurkar, J Zhang, K Lopyrev, P Liang
arXiv preprint arXiv:1606.05250, 2016
75462016
Understanding black-box predictions via influence functions
PW Koh, P Liang
International conference on machine learning, 1885-1894, 2017
27352017
Know what you don't know: Unanswerable questions for SQuAD
P Rajpurkar, R Jia, P Liang
arXiv preprint arXiv:1806.03822, 2018
25562018
On the opportunities and risks of foundation models
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2021
24162021
Prefix-tuning: Optimizing continuous prompts for generation
XL Li, P Liang
arXiv preprint arXiv:2101.00190, 2021
23112021
Semantic parsing on freebase from question-answer pairs
J Berant, A Chou, R Frostig, P Liang
Proceedings of the 2013 conference on empirical methods in natural language …, 2013
20672013
Adversarial examples for evaluating reading comprehension systems
R Jia, P Liang
arXiv preprint arXiv:1707.07328, 2017
16312017
Emergent abilities of large language models
J Wei, Y Tay, R Bommasani, C Raffel, B Zoph, S Borgeaud, D Yogatama, ...
arXiv preprint arXiv:2206.07682, 2022
14202022
Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization
S Sagawa, PW Koh, TB Hashimoto, P Liang
arXiv preprint arXiv:1911.08731, 2019
12982019
Strategies for pre-training graph neural networks
W Hu, B Liu, J Gomes, M Zitnik, P Liang, V Pande, J Leskovec
arXiv preprint arXiv:1905.12265, 2019
11262019
Stanford alpaca: An instruction-following llama model
R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ...
10812023
Wilds: A benchmark of in-the-wild distribution shifts
PW Koh, S Sagawa, H Marklund, SM Xie, M Zhang, A Balsubramani, ...
International Conference on Machine Learning, 5637-5664, 2021
10702021
Certified defenses against adversarial examples
A Raghunathan, J Steinhardt, P Liang
arXiv preprint arXiv:1801.09344, 2018
10332018
QuAC: Question answering in context
E Choi, H He, M Iyyer, M Yatskar, W Yih, Y Choi, P Liang, L Zettlemoyer
arXiv preprint arXiv:1808.07036, 2018
7972018
Certified defenses for data poisoning attacks
J Steinhardt, PWW Koh, PS Liang
Advances in neural information processing systems 30, 2017
7882017
Dropout training as adaptive regularization
S Wager, S Wang, PS Liang
Advances in neural information processing systems 26, 2013
7172013
Learning dependency-based compositional semantics
P Liang, MI Jordan, D Klein
Computational Linguistics 39 (2), 389-446, 2013
7062013
Unlabeled data improves adversarial robustness
Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang
Advances in neural information processing systems 32, 2019
6932019
Semantic parsing via paraphrasing
J Berant, P Liang
Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014
6672014
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
6102022
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