| 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 | 1520 | 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 | 995 | 2019 |
| 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 | 631 | 2022 |
| Fairness without demographics in repeated loss minimization T Hashimoto, M Srivastava, H Namkoong, P Liang International Conference on Machine Learning, 1929-1938, 2018 | 485 | 2018 |
| Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape RI Sherwood, T Hashimoto, CW O'donnell, S Lewis, AA Barkal, ... Nature biotechnology 32 (2), 171-178, 2014 | 467 | 2014 |
| Stanford alpaca: An instruction-following llama model R Taori, I Gulrajani, T Zhang, Y Dubois, X Li, C Guestrin, P Liang, ... | 453* | 2023 |
| Generating sentences by editing prototypes K Guu, TB Hashimoto, Y Oren, P Liang Transactions of the Association for Computational Linguistics 6, 437-450, 2018 | 313 | 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 | 204 | 2022 |
| Unifying human and statistical evaluation for natural language generation TB Hashimoto, H Zhang, P Liang arXiv preprint arXiv:1904.02792, 2019 | 202 | 2019 |
| Diffusion-lm improves controllable text generation X Li, J Thickstun, I Gulrajani, PS Liang, TB Hashimoto Advances in Neural Information Processing Systems 35, 4328-4343, 2022 | 200 | 2022 |
| A retrieve-and-edit framework for predicting structured outputs TB Hashimoto, K Guu, Y Oren, PS Liang Advances in Neural Information Processing Systems 31, 2018 | 138 | 2018 |
| Large language models can be strong differentially private learners X Li, F Tramer, P Liang, T Hashimoto arXiv preprint arXiv:2110.05679, 2021 | 130 | 2021 |
| Distributionally robust language modeling Y Oren, S Sagawa, TB Hashimoto, P Liang arXiv preprint arXiv:1909.02060, 2019 | 129 | 2019 |
| Long-term persistence and development of induced pancreatic beta cells generated by lineage conversion of acinar cells GWQZ Weida Li, Claudia Cavelti-Weder, Yinying Zhang, Kendell Clement, Scott ... Nature Biotechnology 32, 1223-1230, 2014 | 110 | 2014 |
| The gem benchmark: Natural language generation, its evaluation and metrics S Gehrmann, T Adewumi, K Aggarwal, PS Ammanamanchi, ... arXiv preprint arXiv:2102.01672, 2021 | 108 | 2021 |
| The disagreement deconvolution: Bringing machine learning performance metrics in line with reality ML Gordon, K Zhou, K Patel, T Hashimoto, MS Bernstein Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems …, 2021 | 98 | 2021 |
| Word embeddings as metric recovery in semantic spaces TB Hashimoto, D Alvarez-Melis, TS Jaakkola Transactions of the Association for Computational Linguistics 4, 273-286, 2016 | 94 | 2016 |
| Jury learning: Integrating dissenting voices into machine learning models ML Gordon, MS Lam, JS Park, K Patel, J Hancock, T Hashimoto, ... Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems …, 2022 | 86 | 2022 |
| Improved natural language generation via loss truncation D Kang, T Hashimoto arXiv preprint arXiv:2004.14589, 2020 | 71 | 2020 |
| Distributionally robust losses against mixture covariate shifts JC Duchi, T Hashimoto, H Namkoong Under review 2 (1), 2019 | 70 | 2019 |
Percy LiangAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
