arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2402.02716cs.AIcs.CLcs.LG
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理解LLM智能体的规划:一项综述

Understanding the planning of LLM agents: A survey

University of Science and Technology of China(中国科学技术大学);Huawei Noah’s Ark Lab(华为诺亚方舟实验室)

Xu Huang, Weiwen Liu, Xiaolong Chen, Xingmei Wang, Hao Wang, Defu Lian, Yasheng Wang, Ruiming Tang, Enhong Chen

AI总结:

本综述首次系统梳理了基于LLM的智能体规划,提出任务分解、计划选择、外部模块、反思和记忆五类分类,并分析了各方向进展与挑战。

摘要:

随着大型语言模型(LLMs)展现出显著的智能,利用LLMs作为自主智能体规划模块的进展已引起更多关注。本综述首次系统性地审视了基于LLM的智能体规划,涵盖了近期旨在提升规划能力的工作。我们提供了对LLM智能体规划现有工作的分类,可归类为任务分解、计划选择、外部模块、反思和记忆。对每个方向进行了全面分析,并讨论了该研究领域面临的进一步挑战。

英文摘要:

As Large Language Models (LLMs) have shown significant intelligence, the progress to leverage LLMs as planning modules of autonomous agents has attracted more attention. This survey provides the first systematic view of LLM-based agents planning, covering recent works aiming to improve planning ability. We provide a taxonomy of existing works on LLM-Agent planning, which can be categorized into Task Decomposition, Plan Selection, External Module, Reflection and Memory. Comprehensive analyses are conducted for each direction, and further challenges for the field of research are discussed.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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