| A survey on in-context learning Q Dong, L Li, D Dai, C Zheng, Z Wu, B Chang, X Sun, J Xu, Z Sui EMNLP 2024, 2022 | 1476 | 2022 |
| Multilingual machine translation with large language models: Empirical results and analysis W Zhu, H Liu, Q Dong, J Xu, S Huang, L Kong, J Chen, L Li Findings of NAACL2024, 2023 | 293* | 2023 |
| Can We Edit Factual Knowledge by In-Context Learning? C Zheng, L Li, Q Dong, Y Fan, Z Wu, J Xu, B Chang EMNLP 2023, 2023 | 139 | 2023 |
| Calibrating Factual Knowledge in Pretrained Language Models Q Dong, D Dai, Y Song, J Xu, Z Sui, L Li Findings of EMNLP 2022, 2022 | 113 | 2022 |
| Unlocking efficiency in large language model inference: A comprehensive survey of speculative decoding H Xia, Z Yang, Q Dong, P Wang, Y Li, T Ge, T Liu, W Li, Z Sui Findings of ACL2024, 2024 | 66 | 2024 |
| Extrapolating large language models to non-english by aligning languages W Zhu, Y Lv, Q Dong, F Yuan, J Xu, S Huang, L Kong, J Chen, L Li arXiv preprint arXiv:2308.04948, 2023 | 45 | 2023 |
| ParaSCI: A large scientific paraphrase dataset for longer paraphrase generation Q Dong, X Wan, Y Cao The 16th Conference of the European Chapter of the Association for …, 2021 | 39 | 2021 |
| Synthetic data (almost) from scratch: Generalized instruction tuning for language models H Li, Q Dong, Z Tang, C Wang, X Zhang, H Huang, S Huang, X Huang, ... arXiv preprint arXiv:2402.13064, 2024 | 30 | 2024 |
| Cuge: A chinese language understanding and generation evaluation benchmark Y Yao, Q Dong, J Guan, B Cao, Z Zhang, C Xiao, X Wang, F Qi, J Bao, ... arXiv preprint arXiv:2112.13610, 2021 | 19* | 2021 |
| Statistical knowledge assessment for large language models Q Dong, J Xu, L Kong, Z Sui, L Li Advances in Neural Information Processing Systems 36, 2024 | 16* | 2024 |
| Neural knowledge bank for pretrained transformers D Dai, W Jiang, Q Dong, Y Lyu, Z Sui CCF International Conference on Natural Language Processing and Chinese …, 2023 | 16 | 2023 |
| Large language model for science: A study on P vs. NP Q Dong, L Dong, K Xu, G Zhou, Y Hao, Z Sui, F Wei arXiv preprint arXiv:2309.05689, 2023 | 15 | 2023 |
| Omni-math: A universal olympiad level mathematic benchmark for large language models B Gao, F Song, Z Yang, Z Cai, Y Miao, Q Dong, L Li, C Ma, L Chen, R Xu, ... arXiv preprint arXiv:2410.07985, 2024 | 11 | 2024 |
| Periodiclora: Breaking the low-rank bottleneck in lora optimization X Meng, D Dai, W Luo, Z Yang, S Wu, X Wang, P Wang, Q Dong, L Chen, ... arXiv preprint arXiv:2402.16141, 2024 | 10 | 2024 |
| Can Language Models Understand Physical Concepts? L Li, J Xu, Q Dong, C Zheng, Q Liu, L Kong, X Sun EMNLP 2023, 2023 | 10* | 2023 |
| Premise-based Multimodal Reasoning: Conditional Inference on Joint Textual and Visual Clues Q Dong, Z Qin, H Xia, T Feng, S Tong, H Meng, L Xu, W Zhan, S Li, Z Wei, ... Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 9 | 2022 |
| Imagenetvc: Zero-shot visual commonsense evaluation on 1000 imagenet categories H Xia, Q Dong, L Li, J Xu, Z Qin, Z Sui Findings of EMNLP 2023, 2023 | 8 | 2023 |
| Statistical dataset evaluation: A case study on named entity recognition C Wang, Q Dong, X Wang, Z Sui Natural Language Processing 31 (1), 90-110, 2025 | 6* | 2025 |
| Towards a unified view of preference learning for large language models: A survey B Gao, F Song, Y Miao, Z Cai, Z Yang, L Chen, H Hu, R Xu, Q Dong, ... arXiv preprint arXiv:2409.02795, 2024 | 6 | 2024 |
| A Challenging Benchmark for Low-Resource Learning Y Wang, C Ma, Q Dong, L Kong, J Xu Findings of ACL2024, 2023 | 5* | 2023 |
Jingjing XuShanghai AI Lab在 pku.edu.cn 的电子邮件经过验证