| 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 ACL 2024 Findings, 2024 | 68 | 2024 |
| 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 |
| Not All Demonstration Examples are Equally Beneficial: Reweighting Demonstration Examples for In-Context Learning Z Yang, D Dai, P Wang, Z Sui EMNLP 2023 Findings, 2023 | 9 | 2023 |
| 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 |
| Can Large Language Models Always Solve Easy Problems if They Can Solve Harder Ones? Z Yang, Y Zhang, T Liu, J Yang, J Lin, C Zhou, Z Sui EMNLP 2024 main, 2024 | 5 | 2024 |
| Confidence vs Critique: A Decomposition of Self-Correction Capability for LLMs Z Yang, Y Zhang, Y Wang, Z Xu, J Lin, Z Sui arXiv preprint arXiv:2412.19513, 2024 | | 2024 |