Tool Learning in the Wild: Empowering Language Models as Automatic Tool Agents

Abstract

Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, enabling them to solve practical tasks.Previous methods manually parse tool documentation and create in-context demonstrations, transforming tools into structured formats for LLMs to use in their step-by-step reasoning.However, this manual process requires domain expertise and struggles to scale to large toolsets.Additionally, these methods rely heavily on ad-hoc inference techniques or special tokens to integrate free-form LLM generation with tool-calling actions, limiting the LLM's flexibility in handling diverse tool specifications and integrating multiple tools.In this work, we propose AutoTools, a framework that enables LLMs to automate the tool-use workflow.Specifically, the LLM automatically transforms tool documentation into callable functions, verifying syntax and runtime correctness.Then, the LLM integrates these functions into executable programs to solve practical tasks, flexibly grounding tool-use actions into its reasoning processes.Extensive experiments on existing and newly collected, more challenging benchmarks illustrate the superiority of our framework.Inspired by these promising results, we further investigate how to improve the expertise of LLMs, especially opensource LLMs with fewer parameters, within AutoTools.Thus, we propose the AutoTools-Learning approach, training the LLMs with three learning tasks on 34k instances of high-quality synthetic data, including documentation understanding, relevance learning, and function programming.Fine-grained results validate the effectiveness of our overall training approach and each individual task.

Authors 9

1. Zhengliang Shi

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

2. Shen Gao

   University of Electronic Science and Technology of China

   Affiliation as printed

   University of Electronic Science and Technology of China, Chengdu, China

3. Lingyong Yan

   Baidu (China)

   Affiliation as printed

   Baidu Inc., Beijing, China

4. Yue Feng

   University of Birmingham

   Affiliation as printed

   University of Birmingham, Birmingham, United Kingdom

5. Xiuyi Chen

   Baidu (China)

   Affiliation as printed

   Baidu Inc., Beijing, China

6. Zhumin Chen

   Shandong University

   Affiliation as printed

   Shandong University, Qingdao, China

7. Dawei Yin

   Baidu (China)

   Affiliation as printed

   Baidu Inc., Beijing, China

8. Suzan Verberne Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands

9. Zhaochun Ren Aachen

   Leiden University

   Affiliation as printed

   Leiden University, Leiden, Netherlands