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 17 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, such
as ensuring safe and trustworthy tool use, enabling tool creation with foundation models,
Tool Learning with Foundation Models