| Squad: 100,000+ questions for machine comprehension of text P Rajpurkar, J Zhang, K Lopyrev, P Liang arXiv preprint arXiv:1606.05250, 2016 | 7730 | 2016 |
| Understanding black-box predictions via influence functions PW Koh, P Liang International conference on machine learning, 1885-1894, 2017 | 2828 | 2017 |
| 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 | 2730 | 2021 |
| Know what you don't know: Unanswerable questions for SQuAD P Rajpurkar, R Jia, P Liang arXiv preprint arXiv:1806.03822, 2018 | 2668 | 2018 |
| Prefix-tuning: Optimizing continuous prompts for generation XL Li, P Liang arXiv preprint arXiv:2101.00190, 2021 | 2640 | 2021 |
| 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 | 2110 | 2013 |
| Adversarial examples for evaluating reading comprehension systems R Jia, P Liang arXiv preprint arXiv:1707.07328, 2017 | 1656 | 2017 |
| 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 | 1403 | 2022 |
| 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 | 1399 | 2019 |
| Stanford alpaca: An instruction-following llama model R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ... | 1308 | 2023 |
| 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 | 1204 | 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 | 1159 | 2021 |
| Certified defenses against adversarial examples A Raghunathan, J Steinhardt, P Liang arXiv preprint arXiv:1801.09344, 2018 | 1043 | 2018 |
| 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 | 834 | 2018 |
| Certified defenses for data poisoning attacks J Steinhardt, PWW Koh, PS Liang Advances in neural information processing systems 30, 2017 | 812 | 2017 |
| 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 | 732 | 2022 |
| Dropout training as adaptive regularization S Wager, S Wang, PS Liang Advances in neural information processing systems 26, 2013 | 729 | 2013 |
| Unlabeled data improves adversarial robustness Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang Advances in neural information processing systems 32, 2019 | 709 | 2019 |
| Learning dependency-based compositional semantics P Liang, MI Jordan, D Klein Computational Linguistics 39 (2), 389-446, 2013 | 706 | 2013 |
| Generative agents: Interactive simulacra of human behavior JS Park, J O'Brien, CJ Cai, MR Morris, P Liang, MS Bernstein Proceedings of the 36th Annual ACM Symposium on User Interface Software and …, 2023 | 682 | 2023 |
