| 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 | 2774 | 2021 |
| Understanding the Capabilities, Limitations, and Societal Impact of Large Language Models A Tamkin, M Brundage, J Clark, D Ganguli arXiv preprint arXiv:2102.02503, https://arxiv.org/abs/2102.02503, 2021 | 223* | 2021 |
| Being Optimistic to Be Conservative: Quickly Learning a CVaR Policy R Keramati, C Dann, A Tamkin, E Brunskill Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), 2020 | 82 | 2020 |
| Viewmaker Networks: Learning Views for Unsupervised Representation Learning A Tamkin, M Wu, N Goodman ICLR 2021, 2020 | 62 | 2020 |
| Towards measuring the representation of subjective global opinions in language models E Durmus, K Nyugen, TI Liao, N Schiefer, A Askell, A Bakhtin, C Chen, ... arXiv preprint arXiv:2306.16388, 2023 | 58 | 2023 |
| Drone.io: A Gestural and Visual Interface for Human-Drone Interaction JR Cauchard, A Tamkin, CY Wang, L Vink, M Park, T Fang, JA Landay 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI …, 2019 | 55 | 2019 |
| Towards Monosemanticity: Decomposing Language Models With Dictionary Learning T Bricken, A Templeton, J Batson, B Chen, A Jermyn, T Conerly, ... https://transformer-circuits.pub/2023/monosemantic-features/index.html, 2023 | 49 | 2023 |
| Studying large language model generalization with influence functions R Grosse, J Bae, C Anil, N Elhage, A Tamkin, A Tajdini, B Steiner, D Li, ... arXiv preprint arXiv:2308.03296, 2023 | 45 | 2023 |
| Investigating transferability in pretrained language models A Tamkin, T Singh, D Giovanardi, N Goodman Findings of EMNLP 2020, 2020 | 43 | 2020 |
| Language Through a Prism: A Spectral Approach for Multiscale Language Representations A Tamkin, D Jurafsky, N Goodman NeurIPS 2020, 2020 | 35 | 2020 |
| Distributionally-Aware Exploration for CVaR Bandits A Tamkin, R Keramati, C Dann, E Brunskill NeurIPS 2019 Workshop on Safety and Robustness in Decision Making, 2019 | 35 | 2019 |
| DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning A Tamkin, V Liu, R Lu, D Fein, C Schultz, N Goodman NeurIPS 2021, 2021 | 32 | 2021 |
| Active Learning Helps Pretrained Models Learn the Intended Task A Tamkin, D Nguyen, S Deshpande, J Mu, N Goodman NeurIPS 2022, 2022 | 30 | 2022 |
| Recursive Routing Networks: Learning to Compose Modules for Language Understanding I Cases, C Rosenbaum, M Riemer, A Geiger, T Klinger, A Tamkin, O Li, ... NAACL 2019, 2019 | 29 | 2019 |
| C5t5: Controllable generation of organic molecules with transformers D Rothchild, A Tamkin, J Yu, U Misra, J Gonzalez arXiv preprint arXiv:2108.10307, 2021 | 24 | 2021 |
| Task Ambiguity in Humans and Language Models A Tamkin, K Handa, A Shrestha, N Goodman ICLR 2023, 2023 | 20 | 2023 |
| Oolong: Investigating what makes transfer learning hard with controlled studies Z Wu, A Tamkin, I Papadimitriou Proceedings of the 2023 Conference on Empirical Methods in Natural Language …, 2023 | 13* | 2023 |
| Eliciting human preferences with language models BZ Li, A Tamkin, N Goodman, J Andreas arXiv preprint arXiv:2310.11589, 2023 | 12 | 2023 |
| Evaluating and mitigating discrimination in language model decisions A Tamkin, A Askell, L Lovitt, E Durmus, N Joseph, S Kravec, K Nguyen, ... arXiv preprint arXiv:2312.03689, 2023 | 11 | 2023 |
| DABS 2.0: Improved Datasets and Algorithms for Universal Self-Supervision A Tamkin, G Banerjee, M Owda, V Liu, S Rammoorthy, N Goodman NeurIPS 2022, 2022 | 8 | 2022 |
Noah D. GoodmanStanford UniversityVerified email at stanford.edu
