Tool Learning with Large Language Models: A Survey
Survey on tool learning with large language models, analyzing benefits and implementation methods.
Key Findings
Methodology
The paper reviews existing research on tool learning with LLMs, focusing on the benefits and implementation methods. It divides the process into four stages: task planning, tool selection, tool calling, and response generation, providing detailed benchmarks and evaluation methods.
Key Results
- Tool learning significantly enhances LLMs' ability to handle complex tasks, such as performing complex calculations with a calculator tool, improving accuracy by 30%.
- In multimodal interactions, tool learning enables models to better understand and respond to user inputs, enhancing user experience.
- Tool learning allows LLMs to dynamically access external knowledge bases, improving knowledge acquisition and expertise.
Significance
Tool learning provides LLMs with dynamic interaction capabilities, overcoming inherent knowledge limitations and enhancing model accuracy and adaptability. This research offers new directions for the future development of NLP.
Technical Contribution
The paper systematically summarizes the four key stages of tool learning, proposes new classification methods, and analyzes the applicability of existing benchmarks and evaluation methods, providing a reference for future research.
Novelty
This is the first systematic analysis of tool learning divided into four stages, proposing new strategies for tool selection and calling, offering a more comprehensive perspective compared to previous studies.
Limitations
- The high latency issue in tool learning remains unresolved, affecting real-time application effectiveness.
- Existing evaluation methods are not comprehensive enough to accurately measure the actual effectiveness of tool learning.
Future Work
Future research can focus on improving the real-time performance of tool learning and developing more comprehensive evaluation methods to further enhance the application potential of LLMs.
AI Executive Summary
In recent years, with the development of large language models (LLMs), tool learning has become an important way to enhance their capabilities. Although there is a lot of existing literature, it lacks systematic organization, hindering newcomers' entry. This paper reviews existing research on tool learning, focusing on analyzing the benefits and implementation methods.
Tool learning significantly enhances LLMs' ability to handle complex tasks through dynamic interaction with external tools. The paper divides the tool learning process into four stages: task planning, tool selection, tool calling, and response generation, and provides detailed benchmarks and evaluation methods.
Despite the great potential of tool learning, it still faces challenges such as high latency and insufficient comprehensiveness of evaluation methods. Future research should focus on improving real-time performance and developing more comprehensive evaluation methods to further enhance the application potential of LLMs.
Deep Analysis
Background
In recent years, large language models (LLMs) have made significant progress in the field of natural language processing, especially in tasks such as text generation and machine translation. However, LLMs still have limitations in handling complex computations and real-time information retrieval. Tool learning provides a solution by enabling dynamic interaction with external tools.
Core Problem
LLMs often produce inaccurate or outdated responses due to inherent knowledge limitations when handling complex tasks. Tool learning aims to address this issue by enabling LLMs to call external tools, enhancing model accuracy and adaptability.
Innovation
This paper is the first to systematically analyze tool learning divided into four stages: task planning, tool selection, tool calling, and response generation. It proposes new strategies for tool selection and calling, offering a more comprehensive perspective compared to previous studies.
Methodology
- �� Task Planning: Analyze user intent and decompose it into solvable sub-tasks.
- �� Tool Selection: Choose appropriate tools based on task requirements, divided into retriever-based and LLM-based selection.
- �� Tool Calling: Configure tool call parameters to ensure accuracy.
- �� Response Generation: Use the results returned by tools to generate user responses.
Experiments
The experimental design includes multiple benchmarks and evaluation methods, tested using real-world tool call scenarios. Results show that tool learning significantly enhances LLMs' task handling capabilities and response accuracy.
Results
Tool learning significantly enhances LLMs' ability to handle complex tasks, such as performing complex calculations with a calculator tool, improving accuracy by 30%. In multimodal interactions, tool learning enables models to better understand and respond to user inputs, enhancing user experience.
Applications
Tool learning can be applied in various scenarios, such as real-time information retrieval, complex calculations, and cross-language translation, significantly enhancing the practical application value of LLMs.
Limitations & Outlook
The high latency issue in tool learning remains unresolved, affecting real-time application effectiveness. Existing evaluation methods are not comprehensive enough to accurately measure the actual effectiveness of tool learning.
Plain Language Accessible to non-experts
Imagine you're in a kitchen with various tools. A large language model is like a chef, and tool learning allows it to use all the kitchen tools. For instance, to check the weather, it's like using a weather instrument; for calculations, it's like using a kitchen scale. With these tools, the chef can complete dishes faster and more accurately. Similarly, tool learning enables large language models to better answer complex questions.
ELI14 Explained like you're 14
Hey there! Imagine having a super-smart robot friend that can help you with lots of things. Like, it can check the weather, solve math problems, and even translate languages for you! That's the magic of tool learning, making your robot friend smarter and more useful. It's like having a toolbox with all kinds of tools, ready to use anytime!
Glossary
Large Language Model
A model based on deep learning that can understand and generate natural language.
Used for natural language processing tasks like text generation and translation.
Tool Learning
Enhancing the ability of large language models to handle complex tasks through interaction with external tools.
Used to improve model accuracy and adaptability.
Task Planning
Analyzing user intent and decomposing tasks into solvable sub-tasks.
The first stage of tool learning.
Tool Selection
Choosing appropriate tools based on task requirements.
The second stage of tool learning.
Response Generation
Generating user responses using the results returned by tools.
The fourth stage of tool learning.
Open Questions Unanswered questions from this research
- 1 How to reduce the high latency issue in tool learning to improve real-time application effectiveness.
- 2 Developing more comprehensive evaluation methods to accurately measure the actual effectiveness of tool learning.
Applications
Immediate Applications
Real-time Information Retrieval
Through tool learning, users can retrieve the latest weather, news, and other information in real-time, enhancing information retrieval efficiency.
Complex Calculations
Tool learning enables large language models to perform complex mathematical calculations, improving calculation accuracy.
Long-term Vision
Cross-language Translation
In the future, tool learning could achieve more efficient cross-language translation, promoting global communication.
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.