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publications
PreAct: Prediction Enhances Agent’s Planning Ability
Published in COLING 2025, 2024
本文提出了PreAct方法，通过预测来增强智能体的规划能力。
Recommended citation: Fu, D., Huang, J., Lu, S., Dong, G., Wang, Y., He, K., & Xu, W. (2025). PreAct: Prediction Enhances Agent's Planning Ability. In Proceedings of the 2025 International Conference on Computational Linguistics (COLING 2025).
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On Large Language Models’ Hallucination with Regard to Known Facts
Published in NAACL 2024, 2024
本文研究了大语言模型在已知事实方面的幻觉现象。
Recommended citation: Jiang, C., Qi, B., Hong, X., Fu, D., Cheng, Y., Meng, F., Yu, M., Zhou, B., & Zhou, J. (2024). On Large Language Models' Hallucination with Regard to Known Facts. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics.
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CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery
Published in ICLR 2025, 2024
本文提出了一个全面的计算机科学基准测试集CS-Bench，用于评估大语言模型的计算机科学能力。
Recommended citation: Song, X., Diao, M., Dong, G., Wang, Z., Fu, Y., Qiao, R., Wang, Z., Fu, D., Wu, H., Liang, B., Zeng, W., Wang, Y., GongQue, Z., Yu, J., Tan, Q., & Xu, W. (2025). CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery. In International Conference on Learning Representations (ICLR 2025).
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How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data
Published in EMNLP 2024, 2024
本文探讨了如何通过高质量数据来增强代码语言模型的指令调优。
Recommended citation: Wang, Y.*, He, K.*, Fu, D.*, Gongque, Z., Xu, H., Chen, Y., Wang, Z., Fu, Y., Dong, G., Diao, M., Wang, J., Zhang, M., Cai, X., & Xu, W. (2024). How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality Data. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.
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MSI-Agent: Incorporating Multi-Scale Insight into Embodied Agents for Superior Planning and Decision-Making
Published in EMNLP 2024, 2024
本文提出了一种多尺度洞察的具身智能体MSI-Agent，用于提升规划和决策能力。
Recommended citation: Fu, D., Qi, B., Gao, Y., Jiang, C., Dong, G., & Zhou, B. (2024). MSI-Agent: Incorporating Multi-Scale Insight into Embodied Agents for Superior Planning and Decision-Making. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.
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AgentRefine: Enhancing Agent Generalization through Refinement Tuning
Published in ICLR 2025, 2025
本文提出了一种新的Agent微调方法AgentRefine，通过细化调整来增强Agent的泛化能力。
Recommended citation: Fu, D., He, K., Wang, Y., Hong, W., Gongque, Z., Zeng, W., Wang, W., Wang, J., Cai, X., & Xu, W. (2025). AgentRefine: Enhancing Agent Generalization through Refinement Tuning. In International Conference on Learning Representations (ICLR 2025).
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DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments
Published in Working in Progress, 2025
本文提出了DeepResearcher，一个基于强化学习的深度研究辅助系统。
Recommended citation: Zheng, Y.*, Fu, D.*, Hu, X.*, Cai, X., Ye, L., Lu, P., & Liu, P. (2024). DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments. arXiv preprint arXiv:2504.03160.
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talks
MSI-Agent Talk
Published:
This talk introduces the core concepts of MSI-Agent and its applications in computer use. We will explore:
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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