大模型智能体在人工智能领域已展现出巨大的潜力和强大的能力。目前大多数大模型智能体工作都基于闭源大模型如 ChatGPT 或 GPT-4，而基于开源大模型的智能体大多也需要依靠ChatGPT或GPT-4生成的交互轨迹数据来进微调。本次报告将介绍一种基于自规划（Self-Plan）的智能体框架AutoAct, 其仅依靠开源模型自身来生成合成数据，并通过合成轨迹和自主分化来实现多智能体自主协同以完成任务，并在最后探讨知识与大模型智能体的融合之道。
张宁豫，博士，浙江大学副教授，博士生导师，智能科学与工业软件所副所长。研究方向包括知识图谱、自然语言处理等，在知识图谱和自然语言处理领域已发表多篇顶级会议和期刊文章，包括ACL、EMNLP、NAACL、NeurIPS、ICLR等，Google学术引用三千余次，五篇论文入选Paper Digest高影响力论文，获浙江省科技进步二等奖，国际会议IJCKG最佳论文/提名2次，CCKS最佳论文奖1次；担任ACL、EMNLP领域主席、ARR Action Editor、IJCAI 高级程序委员、ACM Transactions on Asian and Low-Resource Language Information Processing Associate Editor及NeurIPS、ICLR、ICML等会议程序委员会委员。
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