Squad: 100,000+ questions for machine comprehension of text P Rajpurkar arXiv preprint arXiv:1606.05250, 2016 | 8618 | 2016 |
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 | 3758 | 2021 |
Prefix-tuning: Optimizing continuous prompts for generation XL Li, P Liang arXiv preprint arXiv:2101.00190, 2021 | 3441 | 2021 |
Understanding black-box predictions via influence functions PW Koh, P Liang International conference on machine learning, 1885-1894, 2017 | 3165 | 2017 |
Know what you don't know: Unanswerable questions for SQuAD P Rajpurkar, R Jia, P Liang arXiv preprint arXiv:1806.03822, 2018 | 2968 | 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 | 2234 | 2013 |
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 | 2096 | 2022 |
Stanford alpaca: An instruction-following llama model R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ... | 1850 | 2023 |
Adversarial examples for evaluating reading comprehension systems R Jia, P Liang arXiv preprint arXiv:1707.07328, 2017 | 1757 | 2017 |
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 | 1634 | 2019 |
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 | 1455 | 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 | 1355 | 2021 |
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 | 1154 | 2023 |
Certified defenses against adversarial examples A Raghunathan, J Steinhardt, P Liang arXiv preprint arXiv:1801.09344, 2018 | 1111 | 2018 |
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 | 999 | 2022 |
Holistic evaluation of language models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... arXiv preprint arXiv:2211.09110, 2022 | 934 | 2022 |
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 | 926 | 2018 |
Certified defenses for data poisoning attacks J Steinhardt, PWW Koh, PS Liang Advances in neural information processing systems 30, 2017 | 885 | 2017 |
Unlabeled data improves adversarial robustness Y Carmon, A Raghunathan, L Schmidt, JC Duchi, PS Liang Advances in neural information processing systems 32, 2019 | 788 | 2019 |
Dropout training as adaptive regularization S Wager, S Wang, PS Liang Advances in neural information processing systems 26, 2013 | 753 | 2013 |