tasks. A highly successful example is ChatGPT, where all the natural language understanding and generation
processes are accomplished through conversational interactions.
Nevertheless, there exist numerous tasks that transcend the scope of purely natural language. For instance,
generating presentation slides2, constructing 3D models via CAD applications, and scheduling meetings
through the analysis of team member calendars are examples of complex tasks that have not been deﬁned in
traditional artiﬁcial intelligence. Fortunately, the strong generalization ability of PLM enables us to use natural
language as a medium to accomplish these tasks by manipulating tools (Zeng et al., 2022). Essentially, the
key to tool learning is to decompose complex tasks into sub-actions, tokenize actions in the form of natural
language and convert them into executable instructions that can be understood by speciﬁc tools. Language
Tool Learning with Foundation Models