Tool learning with large language models: a survey

By Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu, Jirong Wen. Frontiers of Computer Science, 2024

297 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/0479547a69ffa48fe106137eba8358a9d8a2cc48

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In Artificial Intelligence

1. Kelvin Xu
2. Aviral Kumar
3. Charlie Victor Snell
4. Jaehoon Lee

Citations in the corpus

• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters (2024, background): “A number of related works have proposed augmenting language models with tools at test-time, which can greatly improve their performance on downstream tasks [11, 26, 27].”
• Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning (2025, unknown): “Alternatively, LLMs can be prompted or trained to utilize tools, including search engines, as part of their reasoning process (Qu et al., 2025; Trivedi et al., 2022a).”
• TÜLU 3: Pushing Frontiers in Open Language Model Post-Training (2024, unknown): “While we evaluate T ÜLU 3 on its own, LMs are being increasingly deployed as parts of larger systems, in which they have access to tools [Qu et al., 2024] or are themselves part of a larger ‘agent’ framework.”
• A Survey on Vision--Language--Action Models for Embodied AI (2024, unknown): “It receives sequences of states and actions as input, and predicts each state and action token autoregressively.”