[Paper Review] Tool Learning with Large Language Models: A Survey

Changle Qu, Sunhao Dai+6 more|arXiv (Cornell University)|May 28, 2024

Natural Language Processing Techniques4 citations

TL;DR

This survey provides a comprehensive overview of tool learning with large language models (LLMs), systematically analyzing why tool integration enhances LLM capabilities—such as reducing hallucinations and enabling complex reasoning—and how it is implemented through a four-stage workflow: task planning, tool selection, tool calling, and response generation. The key contribution is a unified taxonomy, benchmark categorization, and identification of open challenges, offering a foundational resource for researchers and developers in the field.

ABSTRACT

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. We also maintain a GitHub repository to continually keep track of the relevant papers and resources in this rising area at https://github.com/quchangle1/LLM-Tool-Survey.

Motivation & Objective

• To address the fragmented state of research on tool learning with LLMs by providing a systematic, structured overview.
• To clarify the benefits of tool integration and the tool learning paradigm across six specific dimensions.
• To decompose the tool learning pipeline into four core stages: task planning, tool selection, tool calling, and response generation.
• To categorize and summarize existing benchmarks and evaluation methods according to their relevance to each stage.
• To identify key challenges and propose future research directions in tool learning, including security, unified frameworks, real-world data, and multi-modal integration.

Proposed method

• The survey conducts a systematic literature review of approximately 100 papers on tool learning with LLMs.
• It introduces a four-stage taxonomy for tool learning: task planning, tool selection, tool calling, and response generation.
• It categorizes existing benchmarks and evaluation protocols based on their alignment with each stage of the workflow.
• It analyzes the benefits of tool learning from six perspectives: accuracy, reasoning, real-time knowledge, tool diversity, scalability, and safety.
• It identifies and discusses emerging challenges such as security vulnerabilities, lack of real-world benchmarks, and limited multi-modal support.
• It proposes future research directions, including unified framework development, real-user data collection, and multi-modal tool learning.

Experimental results

Research questions

• RQ1 Why is tool learning beneficial for enhancing the capabilities of large language models?
• RQ2 How can the tool learning process be systematically decomposed into distinct, analyzable stages?
• RQ3 What are the current benchmarks and evaluation methods for tool learning, and how are they distributed across the workflow stages?
• RQ4 What are the major challenges hindering the deployment and scalability of tool-augmented LLMs?
• RQ5 What future research directions are most promising for advancing tool learning with LLMs?

Key findings

• Tool integration significantly reduces hallucinations and improves factual accuracy by enabling LLMs to access external, real-time, or computation-intensive tools.
• The four-stage workflow—task planning, tool selection, tool calling, and response generation—provides a robust and systematic framework for implementing tool learning.
• Existing benchmarks are predominantly based on LLM-generated queries rather than real-world user interactions, limiting their representativeness and practical utility.
• A unified tool learning framework that integrates all four stages is still lacking, leading to fragmented and non-scalable solutions.
• Security remains a critical challenge, as LLMs often lack safety awareness in tool usage, exposing systems to potential exploits.
• Multi-modal tool learning is underexplored, despite growing potential for improving user intent understanding through visual and auditory inputs.