Survey: Tool Learning with Large Language Models

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.

This is the collection of papers related to tool learning with LLMs. These papers are organized according to our survey paper "Tool Learning with Large Language Models: A Survey".

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Contribution

:tada::+1: Please feel free to open an issue or make a pull request! :tada::+1:

Citation

If you find our work helps your research, please kindly cite our paper:

@article{qu2024toolsurvey,
    author={Qu, Changle and Dai, Sunhao and Wei, Xiaochi and Cai, Hengyi and Wang, Shuaiqiang and Yin, Dawei and Xu, Jun and Wen, Ji-Rong},
    title={Tool Learning with Large Language Models: A Survey},
    journal={arXiv preprint arXiv:2405.17935},
    year={2024}
}

📋 Contents

• Survey: Tool Learning with Large Language Models
  • Contribution
  • Citation
  • 📋 Contents
  • 🌟 Introduction
    • The overall workflow for tool learning with large language models.
  • 📄 Paper List
    • Why Tool Learning?
      • Benefit of Tools.
      • Benefit of Tool Learning.
    • How Tool Learning?
      • Task Planning.
      • Tool Selection.
      • Tool Calling.
      • Response Generation.
    • Benchmarks and Evaluation.
      • Benchmarks
      • Evaluation
    • Challenges and Future Directions
    • Other Resources

🌟 Introduction

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.

The overall workflow for tool learning with large language models.

<div align=center> <img src="assets/Framework.png" height="500"/> </div>

📄 Paper List

Why Tool Learning?

Benefit of Tools.

• Knowledge Acquisition.

  • Search Engine

    Internet-Augmented Dialogue Generation, ACL 2022. [Paper]

    WebGPT: Browser-assisted question-answering with human feedback, Preprint 2021. [Paper]

    Internet-augmented language models through few-shot prompting for open-domain question answering, Preprint 2022. [Paper]

    REPLUG: Retrieval-Augmented Black-Box Language Models, Preprint 2023. [Paper]

    Toolformer: Language Models Can Teach Themselves to Use Tools, NeurIPS 2023. [Paper]

    ART: Automatic multi-step reasoning and tool-use for large language models, Preprint 2023. [Paper]

    ToolCoder: Teach Code Generation Models to use API search tools, Preprint 2023. [Paper]

    CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, ICLR 2024. [Paper]

  • Database & Knowledge Graph

    Lamda: Language models for dialog applications, Preprint 2022. [Paper]

    Gorilla: Large Language Model Connected with Massive APIs, Preprint 2023. [Paper]

    ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings, NeurIPS 2023. [Paper]

    ToolQA: A Dataset for LLM Question Answering with External Tools, NeurIPS 2023. [Paper]

    Syntax Error-Free and Generalizable Tool Use for LLMs via Finite-State Decoding, NeurIPS 2023. [Paper]

    Middleware for LLMs: Tools are Instrumental for Language Agents in Complex Environments, Preprint 2024. [Paper]

  • Weather or Map

    On the Tool Manipulation Capability of Open-source Large Language Models, NeurIPS 2023. [Paper]

    ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases, Preprint 2023. [Paper]

    Tool Learning with Foundation Models, Preprint 2023. [Paper]

• Expertise Enhancement.

  • Mathematical Tools

    Training verifiers to solve math word problems, Preprint 2021. [Paper]

    MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning, Preprint 2021. [Paper]

    Chaining Simultaneous Thoughts for Numerical Reasoning, EMNLP 2022. [Paper]

    Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems, EMNLP 2023. [Paper]

    Solving math word problems by combining language models with symbolic solvers, NeurIPS 2023. [Paper]

    Evaluating and improving tool-augmented computation-intensive math reasoning, NeurIPS 2023. [Paper]

    ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving, ICLR 2024. [Paper]

    MATHSENSEI: A Tool-Augmented Large Language Model for Mathematical Reasoning, Preprint 2024. [Paper]

    Calc-CMU at SemEval-2024 Task 7: Pre-Calc -- Learning to Use the Calculator Improves Numeracy in Language Models, NAACL 2024. [Paper]

    MathViz-E: A Case-study in Domain-Specialized Tool-Using Agents, Preprint 2024. [Paper]

  • Python Interpreter

    Pal: Program-aided language models, ICML 2023. [Paper]

    Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks, TMLR 2023. [Paper]

    Fact-Checking Complex Claims with Program-Guided Reasoning, ACL 2023. [Paper]

    Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models, NeurIPS 2023. [Paper]

    LeTI: Learning to Generate from Textual Interactions, NAACL 2024. [Paper]

    Mint: Evaluating llms in multi-turn interaction with tools and language feedback, ICLR 2024. [Paper]

    Executable Code Actions Elicit Better LLM Agents, ICML 2024. [Paper]

    CodeNav: Beyond tool-use to using real-world codebases with LLM agents, Preprint 2024. [Paper]

    APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts, Preprint 2024. [Paper]

    BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions, Preprint 2024. [Paper]

    CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges, ACL 2024. [Paper]

  • Others

    Chemical: MultiTool-CoT: GPT-3 Can Use Multiple External Tools with Chain of Thought Prompting, ACL 2023. [Paper]

    ChemCrow: Augmenting large-language models with chemistry tools, Nature Machine Intelligence 2024. [Paper]

    A REVIEW OF LARGE LANGUAGE MODELS AND AUTONOMOUS AGENTS IN CHEMISTRY, Preprint 2024. [Paper]

    Biomedical: GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information, ISMB 2024. [Paper]

    Financial: Equipping Language Models with Tool Use Capability for Tabular Data Analysis in Finance, EACL 2024. [Paper]

    Financial: Simulating Financial Market via Large Language Model based Agents, Preprint 2024. [Paper]

    Medical: AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale Clinical Tool Learning, Preprint 2024. [Paper]

    MMedAgent: Learning to Use Medical Tools with Multi-modal Agent, Preprint 2024. [Paper]

    Recommendation: Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning, SIGIR 2024. [Paper]

    Gas Turbines: DOMAIN-SPECIFIC ReAct FOR PHYSICS-INTEGRATED ITERATIVE MODELING: A CASE STUDY OF LLM AGENTS FOR GAS PATH ANALYSIS OF GAS TURBINES, Preprint 2024. [Paper]

    WORLDAPIS: The World Is Worth How Many APIs? A Thought Experiment, ACL 2024 Workshop. [Paper]

• Automation and Efficiency.

  • Schedule Tools

    ToolQA: A Dataset for LLM Question Answering with External Tools, NeurIPS 2023. [Paper]

  • Set Reminders

    ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs, ICLR 2024. [Paper]

  • Filter Emails

    ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs, ICLR 2024. [Paper]

  • Project Management

    ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs, ICLR 2024. [Paper]

  • Online Shopping Assistants

    WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents, NeurIPS 2022. [Paper]

• Interaction Enhancement.

  • Multi-modal Tools

    Vipergpt: Visual inference via python execution for reasoning, ICCV 2023. [Paper]

    MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action, Preprint 2023. [Paper]

    InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language, Preprint 2023. [Paper]

    AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn, Preprint 2023. [Paper]

    CLOVA: A closed-loop visual assistant with tool usage and update, CVPR 2024. [Paper]

    DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language Model, CVPR 2024. [Paper]

    MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning, Preprint 2024. [Paper]

    m&m's: A Benchmark to Evaluate Tool-Use for multi-step multi-modal Tasks, Preprint 2024. [Paper]

    From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis, Preprint 2024. [Paper]

  • Machine Translator

    Toolformer: Language Models Can Teach Themselves to Use Tools, NeurIPS 2023. [Paper]

    Tool Learning with Foundation Models, Preprint 2023. [Paper]

  • Natural Language Processing Tools

    HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face, NeurIPS 2023. [Paper]

    GitAgent: Facilitating Autonomous Agent with GitHub by Tool Extension, Preprint 2023. [Paper]

Benefit of Tool Learning.

• Enhanced Interpretability and User Trust.
• Improved Robustness and Adaptability.

How