Tool learning with large language models: a survey

doi:10.1007/s11704-024-40678-2

	Tool learning with large language models: a survey
	Qu, Changle 1; Dai, Sunhao 1; Wei, Xiaochi 2; Cai, Hengyi 3; Wang, Shuaiqiang 2; Yin, Dawei 2; Xu, Jun 1; Wen, Ji-rong 1
	2025-08-01
发表期刊	FRONTIERS OF COMPUTER SCIENCE
ISSN	2095-2228
卷号	19 期号:8 页码:21
摘要	Recently, tool learning with large language models (LLMs) has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems. Despite growing attention and rapid advancements in this field, the existing literature remains fragmented and lacks systematic organization, posing barriers to entry for newcomers. This gap motivates us to conduct a comprehensive survey of existing works on tool learning with LLMs. In this survey, we focus on reviewing existing literature from the two primary aspects (1) why tool learning is beneficial and (2) how tool learning is implemented, enabling a comprehensive understanding of tool learning with LLMs. We first explore the "why" by reviewing both the benefits of tool integration and the inherent benefits of the tool learning paradigm from six specific aspects. In terms of "how", we systematically review the literature according to a taxonomy of four key stages in the tool learning workflow: task planning, tool selection, tool calling, and response generation. Additionally, we provide a detailed summary of existing benchmarks and evaluation methods, categorizing them according to their relevance to different stages. Finally, we discuss current challenges and outline potential future directions, aiming to inspire both researchers and industrial developers to further explore this emerging and promising area.
关键词	tool learning large language models agent
DOI	10.1007/s11704-024-40678-2
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2023YFA1008704] ; National Natural Science Foundation of China[62377044] ; Beijing Key Laboratory of Big Data Management and Analysis Methods, Major Innovation Planning Interdisciplinary Platform for the Double-First Class Initiative, funds for building world-class universities (disciplines) of Renmin University of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:001397127900010
出版者	HIGHER EDUCATION PRESS
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40793
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xu, Jun
作者单位	1.Renmin Univ China, Gaoling Sch Artificial Intelligence, Beijing 100872, Peoples R China 2.Baidu Inc, Beijing 100193, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100864, Peoples R China
推荐引用方式 GB/T 7714	Qu, Changle,Dai, Sunhao,Wei, Xiaochi,et al. Tool learning with large language models: a survey[J]. FRONTIERS OF COMPUTER SCIENCE,2025,19(8):21.
APA	Qu, Changle.,Dai, Sunhao.,Wei, Xiaochi.,Cai, Hengyi.,Wang, Shuaiqiang.,...&Wen, Ji-rong.(2025).Tool learning with large language models: a survey.FRONTIERS OF COMPUTER SCIENCE,19(8),21.
MLA	Qu, Changle,et al."Tool learning with large language models: a survey".FRONTIERS OF COMPUTER SCIENCE 19.8(2025):21.