| Autogen: Enabling next-gen llm applications via multi-agent conversation framework Q Wu, G Bansal, J Zhang, Y Wu, B Li, E Zhu, L Jiang, X Zhang, S Zhang, ... ICLR 2024 Workshop on LLMAgents, 2023 | 275 | 2023 |
| Moderate coreset: A universal method of data selection for real-world data-efficient deep learning X Xia, J Liu, J Yu, X Shen, B Han, T Liu The Eleventh International Conference on Learning Representations, 2022 | 48 | 2022 |
| IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models S Zhang, X Xia, Z Wang, LH Chen, J Liu, Q Wu, T Liu ICLR 2024, 2023 | 13 | 2023 |
| Refined coreset selection: Towards minimal coreset size under model performance constraints X Xia, J Liu, S Zhang, Q Wu, T Liu ICML 2024, 2023 | 6* | 2023 |
| Embodied llm agents learn to cooperate in organized teams X Guo, K Huang, J Liu, W Fan, N Vélez, Q Wu, H Wang, TL Griffiths, ... arXiv preprint arXiv:2403.12482, 2024 | 4 | 2024 |
| Adaptive In-conversation Team Building for Language Model Agents L Song, J Liu, J Zhang, S Zhang, A Luo, S Wang, Q Wu, C Wang arXiv preprint arXiv:2405.19425, 2024 | 1 | 2024 |
| Offline Training of Language Model Agents with Functions as Learnable Weights S Zhang, J Zhang, J Liu, L Song, C Wang, R Krishna, Q Wu Forty-first International Conference on Machine Learning, 0 | 1* | |
Qingyun WuThe Pennsylvania State UniversityVerified email at psu.edu
