  Utilizing complex tools with Large Language Models (LLMs) is a critical
component for grounding AI agents in various real-world scenarios. The core
challenge of manipulating tools lies in understanding their usage and
functionality. The prevailing approach involves few-shot prompting with
demonstrations or fine-tuning on expert trajectories. However, for complex
tools and tasks, mere in-context demonstrations may fail to cover sufficient
knowledge. Training-based methods are also constrained by the high cost of
dataset construction and limited generalizability. In this paper, we introduce
a new tool learning methodology (MetaTool) that is generalizable for mastering
any reusable toolset. Our approach includes a self-supervised data augmentation
technique that enables LLMs to gain a comprehensive understanding of various
tools, thereby improving their ability to complete tasks effectively. We
develop a series of meta-tasks that involve predicting masked factors of tool
execution. These self-supervised tasks enable the automatic generation of
high-quality QA data concerning tool comprehension. By incorporating meta-task
data into the instruction tuning process, the proposed MetaTool model achieves
significant superiority to open-source models and is comparable to
GPT-4/GPT-3.5 on multiple tool-oriented tasks.
