  Humans possess an extraordinary ability to create and utilize tools, allowing
them to overcome physical limitations and explore new frontiers. With the
advent of foundation models, AI systems have the potential to be equally adept
in tool use as humans. This paradigm, i.e., tool learning with foundation
models, combines the strengths of specialized tools and foundation models to
achieve enhanced accuracy, efficiency, and automation in problem-solving.
Despite its immense potential, there is still a lack of a comprehensive
understanding of key challenges, opportunities, and future endeavors in this
field. To this end, we present a systematic investigation of tool learning in
this paper. We first introduce the background of tool learning, including its
cognitive origins, the paradigm shift of foundation models, and the
complementary roles of tools and models. Then we recapitulate existing tool
learning research into tool-augmented and tool-oriented learning. We formulate
a general tool learning framework: starting from understanding the user
instruction, models should learn to decompose a complex task into several
subtasks, dynamically adjust their plan through reasoning, and effectively
conquer each sub-task by selecting appropriate tools. We also discuss how to
train models for improved tool-use capabilities and facilitate the
generalization in tool learning. Considering the lack of a systematic tool
learning evaluation in prior works, we experiment with 18 representative tools
and show the potential of current foundation models in skillfully utilizing
tools. Finally, we discuss several open problems that require further
investigation for tool learning. In general, we hope this paper could inspire
future research in integrating tools with foundation models.
