  While achieving remarkable progress in a broad range of tasks, large language
models (LLMs) remain significantly limited in properly using massive external
tools. Existing in-context learning approaches simply format tools into a list
of plain text descriptions and input them to LLMs, from which, LLMs generate a
sequence of tool calls to solve problems step by step. Such a paradigm ignores
the intrinsic dependency between tools and offloads all reasoning loads to
LLMs, making them restricted to a limited number of specifically designed
tools. It thus remains challenging for LLMs to operate on a library of massive
tools, casting a great limitation when confronted with real-world scenarios.
This paper proposes ToolNet, a plug-and-play framework that scales up the
number of tools to thousands with a moderate increase in token consumption.
ToolNet organizes tools into a directed graph. Each node represents a tool, and
weighted edges denote tool transition. Starting from an initial tool node, an
LLM navigates in the graph by iteratively choosing the next one from its
successors until the task is resolved. Extensive experiments show that ToolNet
can achieve impressive results in challenging multi-hop tool learning datasets
and is resilient to tool failures.
