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| 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 | 4190 | 2021 |
| Prefix-tuning: Optimizing continuous prompts for generation XL Li, P Liang arXiv preprint arXiv:2101.00190, 2021 | 3810 | 2021 |
| Understanding black-box predictions via influence functions PW Koh, P Liang International conference on machine learning, 1885-1894, 2017 | 3281 | 2017 |
| Know what you don't know: Unanswerable questions for SQuAD P Rajpurkar, R Jia, P Liang arXiv preprint arXiv:1806.03822, 2018 | 3068 | 2018 |
| 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 | 2376 | 2022 |
| 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 | 2293 | 2013 |
| Stanford alpaca: An instruction-following llama model R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ... | 2091 | 2023 |
| Adversarial examples for evaluating reading comprehension systems R Jia, P Liang arXiv preprint arXiv:1707.07328, 2017 | 1787 | 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 | 1760 | 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 | 1555 | 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 | 1448 | 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 | 1395 | 2023 |
| 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 | 1140 | 2022 |
| Certified defenses against adversarial examples A Raghunathan, J Steinhardt, P Liang arXiv preprint arXiv:1801.09344, 2018 | 1129 | 2018 |
| 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 | 1064 | 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 | 945 | 2018 |
| Certified defenses for data poisoning attacks J Steinhardt, PWW Koh, PS Liang Advances in neural information processing systems 30, 2017 | 906 | 2017 |
| Lost in the middle: How language models use long contexts NF Liu, K Lin, J Hewitt, A Paranjape, M Bevilacqua, F Petroni, P Liang Transactions of the Association for Computational Linguistics 12, 157-173, 2024 | 904 | 2024 |
| Concept bottleneck models PW Koh, T Nguyen, YS Tang, S Mussmann, E Pierson, B Kim, P Liang International conference on machine learning, 5338-5348, 2020 | 821 | 2020 |
