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Tool Learning with Large Language Models: A Survey

Paper · 2024 · Changle Qu, Sunhao Dai, Xiaochi Wei et al.

Summary

Large language models rely on frozen parametric knowledge. As a result, they often miscalculate or give outdated or invented answers. Connecting them to external tools such as search engines, calculators, code interpreters and APIs is a promising fix, but the research on this has been scattered and hard for newcomers to navigate. This survey organizes the field along two questions.

  • Why use tools? The authors list six benefits. Four come from what tools add: knowledge acquisition, expertise enhancement, automation and efficiency, and richer interaction. Two come from the tool learning paradigm itself: interpretability and user trust, and robustness and adaptability.
  • How is tool use carried out? The authors break the workflow into a sequence of four steps: planning the task, picking a suitable tool, invoking that tool, and finally producing the response. Within the stages, they separate tuning-free methods from tuning-based ones, and retriever-based selection from LLM-based selection.

The survey also maps benchmarks and evaluation metrics to the stage each one measures. It closes with open problems. The result is a structured entry point and reference for researchers and engineers building tool-augmented LLM systems.

Key points

  • The survey covers more than 150 papers. A companion GitHub repository (quchangle1/LLM-Tool-Survey) keeps the paper list current.
  • The authors distinguish two usage paradigms: single invocation and iterative invocation of tools. Representative systems include Toolformer, ReACT, HuggingGPT, ToolLLaMA, Gorilla, ToolkenGPT and RestGPT.
  • Covered benchmarks include ToolBench, APIBench, API-Bank, T-Eval, MetaTool, ToolAlpaca, StableToolBench, ToolQA and AppWorld.
  • Metrics are matched to stages:
    • planning: pass rate and accuracy;
    • selection: Recall, NDCG and COMP;
    • response generation: BLEU, ROUGE-L and Exact Match.
  • The authors name seven open challenges:
    • high latency;
    • more rigorous evaluation;
    • accessible tools;
    • safety and robustness (for example, RoTBench uses five noise levels and ToolSword defines six safety scenarios, and even GPT-4 struggles on them);
    • a unified framework;
    • benchmarks built from real user queries;
    • multi-modal tool learning.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢BaiduBeijing · China84 works · 17,681 citations🎓Renmin University of ChinaBeijing · China139 works · 17,752 citations🏛️Chinese Academy of SciencesBeijing · China246 works · 24,952 citations🔬Institute of Computing TechnologyBeijing · China57 works · 3,782 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

Cite

Cite as Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu and Ji-Rong Wen. 2024. Tool Learning with Large Language Models: A Survey. arXiv:2405.17935.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1007/s11704-024-40678-2 · arXiv 2405.17935

@misc{qu2024tool,
  title = {{Tool Learning with Large Language Models: A Survey}},
  author = {Changle Qu, Sunhao Dai, Xiaochi Wei, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, Jun Xu and Ji-Rong Wen},
  year = {2024},
  doi = {10.1007/s11704-024-40678-2},
  eprint = {2405.17935},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1007/s11704-024-40678-2}
}

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