| 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 | 4594 | 2021 |
| 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 | 3038* | 2022 |
| Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1622 | 2023 |
| 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 | 1149 | 2022 |
| Interpreting pretrained contextualized representations via reductions to static embeddings R Bommasani, K Davis, C Cardie Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020 | 198 | 2020 |
| On the opportunities and risks of foundation models (2021) R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2022 | 153* | 2022 |
| Evaluating human-language model interaction M Lee, M Srivastava, A Hardy, J Thickstun, E Durmus, A Paranjape, ... arXiv preprint arXiv:2212.09746, 2022 | 105 | 2022 |
| The foundation model transparency index R Bommasani, K Klyman, S Longpre, S Kapoor, N Maslej, B Xiong, ... arXiv preprint arXiv:2310.12941, 2023 | 86* | 2023 |
| Intrinsic evaluation of summarization datasets R Bommasani, C Cardie Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 85 | 2020 |
| Picking on the same person: Does algorithmic monoculture lead to outcome homogenization? R Bommasani, KA Creel, A Kumar, D Jurafsky, PS Liang Advances in Neural Information Processing Systems 35, 3663-3678, 2022 | 82 | 2022 |
| On the opportunities and risks of foundation models (arXiv: 2108.07258). arXiv R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... | 78 | 2022 |
| Data governance in the age of large-scale data-driven language technology Y Jernite, H Nguyen, S Biderman, A Rogers, M Masoud, V Danchev, ... Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 69 | 2022 |
| Emergent abilities of large language models. arXiv 2022 J Wei, Y Tay, R Bommasani, C Raffel, B Zoph, S Borgeaud, D Yogatama, ... arXiv preprint arXiv:2206.07682, 2023 | 59 | 2023 |
| Do foundation model providers comply with the draft EU AI Act R Bommasani, K Klyman, D Zhang, P Liang Stanford Center for Research on Foundation Models, https://crfm. stanford …, 2023 | 38 | 2023 |
| On the Societal Impact of Open Foundation Models S Kapoor, R Bommasani, K Klyman, S Longpre, A Ramaswami, P Cihon, ... arXiv preprint arXiv:2403.07918, 2024 | 37* | 2024 |
| Ecosystem graphs: The social footprint of foundation models R Bommasani, D Soylu, TI Liao, KA Creel, P Liang arXiv preprint arXiv:2303.15772, 2023 | 31 | 2023 |
| The time is now to develop community norms for the release of foundation models P Liang, R Bommasani, K Creel, R Reich Protocol, 2022 | 29* | 2022 |
| Reflections on foundation models R Bommasani, P Liang Stanford Institute for Human-Centered AI, 2021 | 25* | 2021 |
| On the opportunities and risks of foundation models; 10.48550 R Bommasani, DA Hudson arXiv preprint ARXIV.2108.07258 4, 2021 | 24* | 2021 |
| Ai regulation has its own alignment problem: The technical and institutional feasibility of disclosure, registration, licensing, and auditing N Guha, C Lawrence, LA Gailmard, K Rodolfa, F Surani, R Bommasani, ... George Washington Law Review, Forthcoming, 2023 | 23 | 2023 |
Percy LiangAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
