| Parameter-efficient fine-tuning of large-scale pre-trained language models N Ding, Y Qin, G Yang, F Wei, Z Yang, Y Su, S Hu, Y Chen, CM Chan, ... Nature Machine Intelligence 5 (3), 220-235, 2023 | 902* | 2023 |
| Chatdev: Communicative agents for software development C Qian, X Cong, C Yang, W Chen, Y Su, J Xu, Z Liu, M Sun ACL 2024, 15174–15186, 2024 | 513* | 2024 |
| Chateval: Towards better llm-based evaluators through multi-agent debate CM Chan, W Chen, Y Su, J Yu, W Xue, S Zhang, J Fu, Z Liu ICLR 2024, 2023 | 350 | 2023 |
| Tool Learning with Foundation Models Y Qin, S Hu, Y Lin, W Chen, N Ding, G Cui, Z Zeng, Y Huang, C Xiao, ... ACM Computing Surveys, 2024 | 264 | 2024 |
| Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors Y Su*, W Chen*, J Zuo, C Yang, C Yuan, CM Chan, H Yu, Y Lu, YH Hung, ... ICLR 2024, 2023 | 254* | 2023 |
| CPM: A large-scale generative Chinese pre-trained language model Z Zhang, X Han, H Zhou, P Ke, Y Gu, D Ye, Y Qin, Y Su, H Ji, J Guan, F Qi, ... AI Open (AI Open 2021) 2, 93-99, 2021 | 203* | 2021 |
| On Transferability of Prompt Tuning for Natural Language Processing Y Su, X Wang, Y Qin, CM Chan, Y Lin, H Wang, K Wen, Z Liu, P Li, J Li, ... NAACL 2022, 3949–3969, 2022 | 151 | 2022 |
| CokeBERT: Contextual knowledge selection and embedding towards enhanced pre-trained language models Y Su, X Han, Z Zhang, P Li, Z Liu, Y Lin, J Zhou, M Sun EMNLP-Finding 2020, 2020 | 68 | 2020 |
| Exploring Universal Intrinsic Task Subspace for Few-shot Learning via Prompt Tuning Y Qin, X Wang, Y Su, Y Lin, N Ding, J Yi, W Chen, Z Liu, J Li, L Hou, P Li, ... IEEE/ACM TASLP 2024, 2024 | 61* | 2024 |
| Knowledge Inheritance for Pre-trained Language Models Y Qin, Y Lin, J Yi, J Zhang, X Han, Z Zhang, Y Su, Z Liu, P Li, M Sun, ... NAACL 2022, 3921--3937, 2022 | 55 | 2022 |
| Confidence matters: Revisiting intrinsic self-correction capabilities of large language models L Li, Z Chen, G Chen, Y Zhang, Y Su, E Xing, K Zhang arXiv preprint arXiv:2402.12563, 2024 | 21 | 2024 |
| Xagent: An autonomous agent for complex task solving X Team https://github.com/OpenBMB/XAgent, 2023 | 11 | 2023 |
| CSS-LM: A Contrastive Framework for Semi-Supervised Fine-Tuning of Pre-Trained Language Models Y Su, X Han, Y Lin, Z Zhang, Z Liu, P Li, J Zhou, M Sun IEEE/ACM TASLP 2021 29, 2930-2941, 2021 | 11 | 2021 |
| Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication W Chen, C Yuan, J Yuan, Y Su, C Qian, C Yang, R Xie, Z Liu, M Sun EMNLP 2024, 10626–10641, 2024 | 7 | 2024 |
| Exploring the impact of model scaling on parameter-efficient tuning Y Su, CM Chan, J Cheng, Y Qin, Y Lin, S Hu, Z Yang, N Ding, X Sun, ... EMNLP 2023, 15062-15078, 2023 | 7* | 2023 |
| Language-specific representation of emotion-concept knowledge causally supports emotion inference M Li, Y Su, HY Huang, J Cheng, X Hu, X Zhang, H Wang, Y Qin, X Wang, ... iScience, 2024 | 6* | 2024 |
| Agent laboratory: Using llm agents as research assistants S Schmidgall, Y Su, Z Wang, X Sun, J Wu, X Yu, J Liu, Z Liu, E Barsoum arXiv preprint arXiv:2501.04227, 2025 | 1 | 2025 |
| Introducing the First AMD 1B Language Models: AMD OLMo J Liu, J Wu, P Mishra, Z Liu, S Ranjan, PP Brahma, Y Su, G Ramesh, ... https://www.amd.com/en/developer/resources/technical-articles/introducing …, 2024 | | 2024 |
Zhiyuan LiuAssociate Professor, Tsinghua University在 tsinghua.edu.cn 的电子邮件经过验证