| Squad: 100,000+ questions for machine comprehension of text P Rajpurkar, J Zhang, K Lopyrev, P Liang arXiv preprint arXiv:1606.05250, 2016 | 6749 | 2016 |
| Understanding black-box predictions via influence functions PW Koh, P Liang International conference on machine learning, 1885-1894, 2017 | 2416 | 2017 |
| Know what you don't know: Unanswerable questions for SQuAD P Rajpurkar, R Jia, P Liang arXiv preprint arXiv:1806.03822, 2018 | 2246 | 2018 |
| 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 | 1930 | 2013 |
| 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 | 1499 | 2021 |
| Adversarial examples for evaluating reading comprehension systems R Jia, P Liang arXiv preprint arXiv:1707.07328, 2017 | 1485 | 2017 |
| Prefix-tuning: Optimizing continuous prompts for generation XL Li, P Liang arXiv preprint arXiv:2101.00190, 2021 | 1482 | 2021 |
| 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 | 990 | 2019 |
| Certified defenses against adversarial examples A Raghunathan, J Steinhardt, P Liang arXiv preprint arXiv:1801.09344, 2018 | 950 | 2018 |
| 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 | 883 | 2019 |
| 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 | 825 | 2021 |
| Certified defenses for data poisoning attacks J Steinhardt, PWW Koh, PS Liang Advances in neural information processing systems 30, 2017 | 703 | 2017 |
| 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 | 697 | 2018 |
| Dropout training as adaptive regularization S Wager, S Wang, PS Liang Advances in neural information processing systems 26, 2013 | 690 | 2013 |
| Learning dependency-based compositional semantics P Liang, MI Jordan, D Klein Computational Linguistics 39 (2), 389-446, 2013 | 687 | 2013 |
| Semantic parsing via paraphrasing J Berant, P Liang Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 649 | 2014 |
| 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 | 617 | 2022 |
| Unlabeled data improves adversarial robustness Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang Advances in neural information processing systems 32, 2019 | 612 | 2019 |
| Alignment by agreement P Liang, B Taskar, D Klein | 560 | 2006 |
| Compositional semantic parsing on semi-structured tables P Pasupat, P Liang arXiv preprint arXiv:1508.00305, 2015 | 531 | 2015 |
