| Why can gpt learn in-context? language models implicitly perform gradient descent as meta-optimizers D Dai, Y Sun, L Dong, Y Hao, S Ma, Z Sui, F Wei arXiv preprint arXiv:2212.10559, 2022 | 207 | 2022 |
| Retentive network: A successor to transformer for large language models Y Sun, L Dong, S Huang, S Ma, Y Xia, J Xue, J Wang, F Wei arXiv preprint arXiv:2307.08621, 2023 | 85 | 2023 |
| A length-extrapolatable transformer Y Sun, L Dong, B Patra, S Ma, S Huang, A Benhaim, V Chaudhary, ... arXiv preprint arXiv:2212.10554, 2022 | 69 | 2022 |
| Prototypical calibration for few-shot learning of language models Z Han, Y Hao, L Dong, Y Sun, F Wei The Eleventh International Conference on Learning Representations, 2022 | 24 | 2022 |
| Structured prompting: Scaling in-context learning to 1,000 examples Y Hao, Y Sun, L Dong, Z Han, Y Gu, F Wei arXiv preprint arXiv:2212.06713, 2022 | 23 | 2022 |
Furu WeiPartner Research Manager, Microsoft Research在 microsoft.com 的电子邮件经过验证
