models serve as “translators”, making complex tasks more accessible to individuals without specialized
technical knowledge. The potential applications of tool learning are vast and exciting, ranging from automated
customer service and personal assistants to self-driving cars and even space exploration. By enabling machines
to understand and interact with human language in a more natural and nuanced way, we can unlock new
possibilities for collaboration and problem-solving that were previously impossible. We anticipate that tool
learning will prove instrumental in facilitating the integration of diverse tasks through shared tooling. Thus,
while natural language interfaces have enabled uniﬁcation within the realm of language (Hao et al., 2022),
the challenges posed by non-linguistic tasks necessitate a more advanced approach to leveraging both natural
language and tool learning. By harnessing the power of natural language, we can create systems that are
Tool Learning with Foundation Models