| 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 | 2037 | 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 | 960 | 2022 |
| Bloom: A 176b-parameter open-access multilingual language model BS Workshop, TL Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, ... arXiv preprint arXiv:2211.05100, 2022 | 706 | 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 | 329 | 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 | 144 | 2020 |
| Intrinsic evaluation of summarization datasets R Bommasani, C Cardie Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 49 | 2020 |
| Evaluating human-language model interaction M Lee, M Srivastava, A Hardy, J Thickstun, E Durmus, A Paranjape, ... arXiv preprint arXiv:2212.09746, 2022 | 40 | 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 | 35 | 2022 |
| 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 | 25 | 2022 |
| Reflections on foundation models R Bommasani, P Liang Stanford Institute for Human-Centered AI, 2021 | 15* | 2021 |
| The time is now to develop community norms for the release of foundation models P Liang, R Bommasani, K Creel, R Reich Protocol, 2022 | 14* | 2022 |
| Ecosystem graphs: The social footprint of foundation models R Bommasani, D Soylu, TI Liao, KA Creel, P Liang arXiv preprint arXiv:2303.15772, 2023 | 8 | 2023 |
| Towards Private Synthetic Text Generation R Bommasani, ZS Wu, A Schofield 2019 NeurIPS Workshop: Machine Learning with Guarantees, 2019 | 8* | 2019 |
| Do foundation model providers comply with the eu ai act R Bommasani, K Klyman, D Zhang, P Liang Center for Research on Foundation Models, 2023 | 7* | 2023 |
| Mistral-a journey towards reproducible language model training, 2021 S Karamcheti, L Orr, J Bolton, T Zhang, K Goel, A Narayan, R Bommasani, ... URL https://github. com/stanford-crfm/mistral, 0 | 6 | |
| The Foundation Model Transparency Index R Bommasani, K Klyman, S Longpre, S Kapoor, N Maslej, B Xiong, ... arXiv preprint arXiv:2310.12941, 2023 | 4 | 2023 |
| Long-Distance Dependencies Don’t Have to Be Long: Simplifying through Provably (Approximately) Optimal Permutations R Bommasani Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 4* | 2019 |
| SPARSE: Structured Prediction using Argument-Relative Structured Encoding R Bommasani, A Katiyar, C Cardie Proceedings of the 2019 NAACL Workshop SPNLP: Structured Prediction for NLP …, 2019 | 4 | 2019 |
| Cheaply Estimating Inference Efficiency Metrics for Autoregressive Transformer Models D Narayanan, K Santhanam, P Henderson, R Bommasani, T Lee, P Liang Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 2* | 2023 |
| AI Spring? Four Takeaways from Major Releases in Foundation Models R Bommasani Stanford Institute for Human-Centered Artificial Intelligence. Retrieved …, 2023 | 2 | 2023 |
Percy LiangAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
