| 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 | 4676 | 2021 |
| Ask me anything: A simple strategy for prompting language models S Arora, A Narayan, MF Chen, L Orr, N Guha, K Bhatia, I Chami, F Sala, ... arXiv preprint arXiv:2210.02441, 2022 | 222 | 2022 |
| Can foundation models wrangle your data? A Narayan, I Chami, L Orr, S Arora, C Ré arXiv preprint arXiv:2205.09911, 2022 | 200 | 2022 |
| On the opportunities and risks of foundation models. arXiv 2021 R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2023 | 107 | 2023 |
| & Liang, P.(2021). 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, 0 | 89 | |
| 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 | 74 | 2022 |
| Language models enable simple systems for generating structured views of heterogeneous data lakes S Arora, B Yang, S Eyuboglu, A Narayan, A Hojel, I Trummer, C Ré arXiv preprint arXiv:2304.09433, 2023 | 67 | 2023 |
| Rekall: Specifying video events using compositions of spatiotemporal labels DY Fu, W Crichton, J Hong, X Yao, H Zhang, A Truong, A Narayan, ... arXiv preprint arXiv:1910.02993, 2019 | 61 | 2019 |
| Perfectly balanced: Improving transfer and robustness of supervised contrastive learning M Chen, DY Fu, A Narayan, M Zhang, Z Song, K Fatahalian, C Ré International Conference on Machine Learning, 3090-3122, 2022 | 51 | 2022 |
| Personalized benchmarking with the ludwig benchmarking toolkit A Narayan, P Molino, K Goel, W Neiswanger, C Re arXiv preprint arXiv:2111.04260, 2021 | 12 | 2021 |
| Neural generation meets real people: Building a social, informative open-domain dialogue agent EA Chi, A Paranjape, A See, C Chiam, T Chang, K Kenealy, SK Lim, ... arXiv preprint arXiv:2207.12021, 2022 | 11 | 2022 |
| Neural, neural everywhere: Controlled generation meets scaffolded, structured dialogue EA Chi, C Chiam, T Chang, SK Lim, C Rastogi, A Iyabor, Y He, ... Alexa Prize Proceedings, 2021 | 11 | 2021 |
| Automating the Enterprise with Foundation Models M Wornow, A Narayan, K Opsahl-Ong, Q McIntyre, NH Shah, C Re arXiv preprint arXiv:2405.03710, 2024 | 10 | 2024 |
| TART: A plug-and-play Transformer module for task-agnostic reasoning K Bhatia, A Narayan, CM De Sa, C Ré Advances in Neural Information Processing Systems 36, 9751-9788, 2023 | 8 | 2023 |
| Do Multimodal Foundation Models Understand Enterprise Workflows? A Benchmark for Business Process Management Tasks M Wornow, A Narayan, B Viggiano, IS Khare, T Verma, T Thompson, ... arXiv preprint arXiv:2406.13264, 2024 | 7 | 2024 |
| Mistral–a journey towards reproducible language model training S Karamcheti, L Orr, J Bolton, T Zhang, K Goel, A Narayan, R Bommasani, ... Palo Alto: Stanford Center for Research on Foundation Models, 2021 | 5 | 2021 |
| Cookbook: A framework for improving LLM generative abilities via programmatic data generating templates A Narayan, MF Chen, K Bhatia, C Ré arXiv preprint arXiv:2410.05224, 2024 | 2 | 2024 |
| Wonderbread: A benchmark for evaluating multimodal foundation models on business process management tasks M Wornow, A Narayan, B Viggiano, IS Khare, T Verma, T Thompson, ... The Thirty-eight Conference on Neural Information Processing Systems …, 0 | 1 | |
| DAMsL: A Meta-Learning Based Approach for Dialogue State Tracking A Narayan, J Hedtke https://web. stanford. edu/class/archive/cs/cs224n/cs224n 1204, 1-10, 0 | 1 | |
| Distributed Collaborative Organisations A Narayan, J Dietz, G Xethalis, C Crawford, D Bollier, H Shadab, ... | | 2014 |
Christopher RéComputer Science, Stanford University確認したメール アドレス: cs.stanford.edu