| Active prompting with chain-of-thought for large language models S Diao*, P Wang*, Y Lin, R Pan, X Liu, T Zhang ACL 2024, 2023 | 197 | 2023 |
| LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning R Pan*, X Liu*, S Diao, R Pi, J Zhang, C Han, T Zhang NeurIPS 2024, 2024 | 36* | 2024 |
| Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models P Dong*, L Li*, Z Tang, X Liu, X Pan, Q Wang, X Chu ICML 2024, 2024 | 30 | 2024 |
| Plum: Prompt learning using metaheuristic R Pan*, S Xing*, S Diao, W Sun, X Liu, K Shum, R Pi, J Zhang, T Zhang ACL 2024 Findings, 2023 | 13 | 2023 |
| Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models L Zhang*, X Liu*, Z Li, X Pan, P Dong, R Fan, R Guo, X Wang, Q Luo, ... arXiv preprint arXiv:2311.03687, 2023 | 7 | 2023 |
| ParZC: Parametric Zero-Cost Proxies for Efficient NAS P Dong*, L Li*, X Pan, Z Wei, X Liu, Q Wang, X Chu AAAI 2024 Oral, 2024 | 5 | 2024 |
| LongGenBench: Long-context Generation Benchmark X Liu, P Dong, X Hu, X Chu EMNLP 2024 Findings, 2024 | 4 | 2024 |
| Should We Really Edit Language Models? On the Evaluation of Edited Language Models Q Li*, X Liu*, Z Tang, P Dong, Z Li, X Pan, X Chu NeurIPS 2024, 2024 | 3 | 2024 |
| Discovering Sparsity Allocation for Layer-wise Pruning of Large Language Models L Li*, P Dong*, Z Tang, X Liu, Q Wang, W Luo, W Xue, Q Liu, X Chu, ... NeurIPS 2024, 2024 | 3 | 2024 |
| LPZero: Language Model Zero-cost Proxy Search from Zero P Dong, L Li, X Liu, Z Tang, X Liu, Q Wang, X Chu EMNLP 2024 Findings, 2024 | 1 | 2024 |
| 3D Question Answering for City Scene Understanding P Sun*, Y Song*, X Liu, X Yang, Q Wang, T Li, Y Yang, X Chu ACM MM 2024, 2024 | 1 | 2024 |
| Mediator: Memory-efficient LLM Merging with Less Parameter Conflicts and Uncertainty Based Routing K Lai, Z Tang, X Pan, P Dong, X Liu, H Chen, L Shen, B Li, X Chu arXiv preprint arXiv:2502.04411, 2025 | | 2025 |
| Can LLMs Maintain Fundamental Abilities under KV Cache Compression? X Liu, Z Tang, H Chen, P Dong, Z Li, X Zhou, B Li, X Hu, X Chu arXiv preprint arXiv:2502.01941, 2025 | | 2025 |
| ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference X Liu, Z Tang, P Dong, Z Li, B Li, X Hu, X Chu arXiv preprint arXiv:2502.00299, 2025 | | 2025 |
Shizhe DiaoNVIDIA ResearchВерификована је имејл адреса на nvidia.com