Rishi Bommasani
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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
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
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
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
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
Intrinsic evaluation of summarization datasets
R Bommasani, C Cardie
Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020
Evaluating human-language model interaction
M Lee, M Srivastava, A Hardy, J Thickstun, E Durmus, A Paranjape, ...
arXiv preprint arXiv:2212.09746, 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
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
Reflections on foundation models
R Bommasani, P Liang
Stanford Institute for Human-Centered AI, 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
Ecosystem graphs: The social footprint of foundation models
R Bommasani, D Soylu, TI Liao, KA Creel, P Liang
arXiv preprint arXiv:2303.15772, 2023
Towards Private Synthetic Text Generation
R Bommasani, ZS Wu, A Schofield
2019 NeurIPS Workshop: Machine Learning with Guarantees, 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
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
The Foundation Model Transparency Index
R Bommasani, K Klyman, S Longpre, S Kapoor, N Maslej, B Xiong, ...
arXiv preprint arXiv:2310.12941, 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
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
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
AI Spring? Four Takeaways from Major Releases in Foundation Models
R Bommasani
Stanford Institute for Human-Centered Artificial Intelligence. Retrieved …, 2023
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