Large Language Models as Tool Makers

Science & Technology

Introduction

In this article, we explore the concept of using Large Language Models (LLMs) as tool makers, allowing them to create their own external tools for problem solving. Traditional approaches rely on existing tools like web searches or calculators to enhance the problem-solving abilities of LLMs. However, this paper delves into a method where LLMs can generate their own tools tailored to specific problem types, all in a closed-loop system. The process involves two LLMs: a tool-making LLM that creates the tools and a tool-using LLM that applies these tools to solve tasks. By utilizing the more capable tool-making LLM initially and then transitioning to a lighter tool-using LLM, this approach proves to be cost-effective and efficient.

The paper outlines a three-step process for building tools: proposing a tool by constructing prompts, validating the tool through unit tests, and packaging the tool for future use. By employing a combination of a heavyweight model (e.g., GPT-4) for tool creation and a lightweight model (e.g. GPT 3.5) for tool utilization, the study demonstrates how the lightweight model can match or even outperform the heavyweight model in solving specific classes of problems. This approach showcases the potential of automated tool generation by LLMs to enhance problem-solving capabilities while reducing costs.

Keywords

Large Language Models, Tool Making, Problem Solving, Automated Tool Generation, Heavyweight Model, Lightweight Model, Prompt Construction, Cost-Effective Solutions

FAQ

  1. What is the primary focus of the paper regarding Large Language Models (LLMs)?

    • The paper explores using LLMs to create their own external tools for problem solving, aiming to reduce dependency on existing tools and enhance problem-solving capabilities.

  2. What is the significance of having a tool-making LLM and a tool-using LLM in this framework?

    • The approach involves using a more capable tool-making LLM for creating tools tailored to specific problem types and then transitioning to a lighter tool-using LLM for efficient problem solving.

  3. How does the three-step process for building tools work in this context?

    • The process involves proposing a tool through prompts, validating the tool with unit tests, and packaging it for future use, showcasing the automation of tool generation by LLMs.