title: Large Language Models as Tool Makers
summary: Recent research shows the potential of enhancing the problem-solving ability
  of large language models (LLMs) through the use of external tools. However,
  prior work along this line depends on the availability of existing tools. In
  this work, we take an initial step towards removing this dependency by
  proposing a closed-loop framework, referred to as LLMs As Tool Makers (LATM),
  where LLMs create their own reusable tools for problem-solving. Our approach
  consists of two key phases: 1) tool making: an LLM acts as the tool maker that
  crafts tools for given tasks, where a tool is implemented as a Python utility
  function. 2) tool using: an LLM acts as the tool user, which applies the tool
  built by the tool maker for problem-solving. The tool user can be either the
  same or a different LLM from the tool maker. Tool-making enables an LLM to
  continually generate tools that can be applied to different requests so that
  future requests can call the corresponding APIs when beneficial for solving the
  tasks. Furthermore, the division of labor among LLMs for tool-making and
  tool-using phases introduces the opportunity to achieve cost effectiveness
  without degrading the quality of generated tools and problem solutions. For
  example, recognizing that tool-making demands more sophisticated capabilities
  than tool-using, we can apply a powerful yet resource-intensive model as the
  tool maker, and a lightweight while cost-effective model as the tool user. We
  validate the effectiveness of our approach across a variety of complex
  reasoning tasks, including Big-Bench tasks. With GPT-4 as the tool maker and
  GPT-3.5 as the tool user, LATM can achieve performance that is on par with
  using GPT-4 for both tool making and tool using, while the inference cost is
  significantly reduced.
paper_authors._id: 647410cb71f07ae738d5188c
paper_authors.name: Tianle Cai
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-05-30T13:20:04.210Z
paper_authors._id: 64459357c525660aa20be337
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/1fryBXRuSvAgMIDkpWBpI.jpeg
paper_authors.fullname: Tianle Cai
paper_authors.type: user
paper_authors.user: tianlecai
paper_authors._id: 647410cb71f07ae738d5188d
paper_authors.name: Xuezhi Wang
paper_authors._id: 647410cb71f07ae738d5188e
paper_authors.name: Tengyu Ma
paper_authors._id: 647410cb71f07ae738d5188f
paper_authors.name: Xinyun Chen
paper_authors._id: 647410cb71f07ae738d51890
paper_authors.name: Denny Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-20T18:28:36.433Z
paper_authors._id: 65d4d62d493aeaf0bb295f1b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/vXMxaoWwFziul8TqIhQw8.jpeg
paper_authors.fullname: Denny Zhou
paper_authors.type: user
paper_authors.user: dennyzhou
paper_title: Large Language Models as Tool Makers
paper_summary: Recent research shows the potential of enhancing the problem-solving ability
  of large language models (LLMs) through the use of external tools. However,
  prior work along this line depends on the availability of existing tools. In
  this work, we take an initial step towards removing this dependency by
  proposing a closed-loop framework, referred to as LLMs As Tool Makers (LATM),
  where LLMs create their own reusable tools for problem-solving. Our approach
  consists of two key phases: 1) tool making: an LLM acts as the tool maker that
  crafts tools for given tasks, where a tool is implemented as a Python utility
  function. 2) tool using: an LLM acts as the tool user, which applies the tool
  built by the tool maker for problem-solving. The tool user can be either the
  same or a different LLM from the tool maker. Tool-making enables an LLM to
  continually generate tools that can be applied to different requests so that
  future requests can call the corresponding APIs when beneficial for solving the
  tasks. Furthermore, the division of labor among LLMs for tool-making and
  tool-using phases introduces the opportunity to achieve cost effectiveness
  without degrading the quality of generated tools and problem solutions. For
  example, recognizing that tool-making demands more sophisticated capabilities
  than tool-using, we can apply a powerful yet resource-intensive model as the
  tool maker, and a lightweight while cost-effective model as the tool user. We
  validate the effectiveness of our approach across a variety of complex
  reasoning tasks, including Big-Bench tasks. With GPT-4 as the tool maker and
  GPT-3.5 as the tool user, LATM can achieve performance that is on par with
  using GPT-4 for both tool making and tool using, while the inference cost is
  significantly reduced.
paper_ai_keywords: large language models (LLMs) closed-loop framework LLMs As Tool Makers (LATM) tool-making tool-using Python utility function division of labor cost effectiveness Big-Bench tasks GPT-4 GPT-3.5 performance