| Scaling instruction-finetuned language models HW Chung, L Hou, S Longpre, B Zoph, Y Tay, W Fedus, Y Li, X Wang, ... Journal of Machine Learning Research 25 (70), 1-53, 2024 | 2632 | 2024 |
| Delving into transferable adversarial examples and black-box attacks Y Liu, X Chen, C Liu, D Song arXiv preprint arXiv:1611.02770, 2016 | 2030 | 2016 |
| Targeted backdoor attacks on deep learning systems using data poisoning X Chen, C Liu, B Li, K Lu, D Song arXiv preprint arXiv:1712.05526, 2017 | 1896 | 2017 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1480 | 2023 |
| Competition-level code generation with alphacode Y Li, D Choi, J Chung, N Kushman, J Schrittwieser, R Leblond, T Eccles, ... Science 378 (6624), 1092-1097, 2022 | 871 | 2022 |
| Adversarial example defense: Ensembles of weak defenses are not strong W He, J Wei, X Chen, N Carlini, D Song 11th USENIX workshop on offensive technologies (WOOT 17), 2017 | 441 | 2017 |
| Large language models as optimizers C Yang, X Wang, Y Lu, H Liu, QV Le, D Zhou, X Chen International Conference on Learning Representations (ICLR) 2024, 2023 | 409* | 2023 |
| Learning to perform local rewriting for combinatorial optimization X Chen, Y Tian Advances in neural information processing systems 32, 2019 | 403 | 2019 |
| Teaching large language models to self-debug X Chen, M Lin, N Schärli, D Zhou arXiv preprint arXiv:2304.05128, 2023 | 366 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 350 | 2024 |
| Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses M Goldblum, D Tsipras, C Xie, X Chen, A Schwarzschild, D Song, ... IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (2), 1563-1580, 2022 | 316* | 2022 |
| Tree-to-tree neural networks for program translation X Chen, C Liu, D Song Advances in neural information processing systems 31, 2018 | 298 | 2018 |
| Large language models can be easily distracted by irrelevant context F Shi, X Chen, K Misra, N Scales, D Dohan, EH Chi, N Schärli, D Zhou International Conference on Machine Learning, 31210-31227, 2023 | 289* | 2023 |
| Larger language models do in-context learning differently J Wei, J Wei, Y Tay, D Tran, A Webson, Y Lu, X Chen, H Liu, D Huang, ... arXiv preprint arXiv:2303.03846, 2023 | 217 | 2023 |
| Large language models cannot self-correct reasoning yet J Huang, X Chen, S Mishra, HS Zheng, AW Yu, X Song, D Zhou arXiv preprint arXiv:2310.01798, 2023 | 171 | 2023 |
| Execution-guided neural program synthesis X Chen, C Liu, D Song International Conference on Learning Representations, 2018 | 157 | 2018 |
| Refit: a unified watermark removal framework for deep learning systems with limited data X Chen, W Wang, C Bender, Y Ding, R Jia, B Li, D Song Proceedings of the 2021 ACM Asia Conference on Computer and Communications …, 2021 | 125* | 2021 |
| Fooling vision and language models despite localization and attention mechanism X Xu, X Chen, C Liu, A Rohrbach, T Darrell, D Song Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 123* | 2018 |
| Neural symbolic reader: Scalable integration of distributed and symbolic representations for reading comprehension X Chen, C Liang, AW Yu, D Zhou, D Song, QV Le International Conference on Learning Representations, 2019 | 121 | 2019 |
| Large language models as tool makers T Cai, X Wang, T Ma, X Chen, D Zhou arXiv preprint arXiv:2305.17126, 2023 | 118 | 2023 |
Dawn SongProfessor of Computer Science, UC BerkeleyVerified email at cs.berkeley.edu
