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

Zhengliang Shi, Shen Gao, Lingyong Yan, Yue Feng, Xiuyi Chen, Zhumin Chen, Dawei Yin, Suzan Verberne, Zhaochun Ren

Track: Semantics and knowledge

Keywords: Large language model, Tool Learning

TL;DR: In this work, we propose AutoTools and AutoTools-learning methods, that enable LLMs to automate the tool-use workflow.

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.
% The LLMs show diminished performance when in-context examples are incomplete.
Additionally, these methods rely heavily on ad-hoc inference technique 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 open-source LLMs with fewer parameters, within AutoTools.
Thus, we propose a method for AutoTools-learning, 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. Our methods are an important step towards the use of LLMs for solving real-world tasks with external tools.

Submission Number: 256