Large language models are human-level prompt engineers Y Zhou, AI Muresanu, Z Han, K Paster, S Pitis, H Chan, J Ba International Conference on Learning Representations (ICLR 2023), 2023 | 617 | 2023 |
Dataset distillation using neural feature regression Y Zhou, E Nezhadarya, J Ba Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 2022 | 105 | 2022 |
Transcriptome-wide off-target effects of steric-blocking oligonucleotides EM Holgersen, S Gandhi, Y Zhou, J Kim, B Vaz, J Bogojeski, M Bugno, ... nucleic acid therapeutics 31 (6), 392-403, 2021 | 54 | 2021 |
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes R Agarwal, N Vieillard, Y Zhou, P Stanczyk, S Ramos, M Geist, O Bachem International Conference on Learning Representations (ICLR 2024), 2024 | 46* | 2024 |
Distillspec: Improving speculative decoding via knowledge distillation Y Zhou, K Lyu, AS Rawat, AK Menon, A Rostamizadeh, S Kumar, JF Kagy, ... International Conference on Learning Representations (ICLR 2024), 2024 | 29 | 2024 |
Identifying the risks of lm agents with an lm-emulated sandbox Y Ruan, H Dong, A Wang, S Pitis, Y Zhou, J Ba, Y Dubois, CJ Maddison, ... International Conference on Learning Representations (ICLR 2024), 2024 | 28 | 2024 |
Training on Thin Air: Improve Image Classification with Generated Data Y Zhou, H Sahak, J Ba ICML Workshop on Data-centric Machine Learning, 2023, 2023 | 22 | 2023 |
Transformers Can Achieve Length Generalization But Not Robustly Y Zhou, U Alon, X Chen, X Wang, R Agarwal, D Zhou arXiv preprint arXiv:2402.09371, 2024 | 8 | 2024 |