real-time tool execution to mitigate limitations in memorization. For instance, a signiﬁcant proportion of the
memorization burden can be ofﬂoaded to the search engine if foundation models can learn how to utilize it. (2)
Enhanced Expertise. Specialized tools are designed to cater to speciﬁc domains with functionalities that are
not available in foundation models. As a result, they are better suited to address the needs of domain-speciﬁc
tasks, such as Wolfram 3 for scientiﬁc calculation, through the utilization of tailored algorithms. Instead
of solely relying on the foundation model to accomplish the task, models could invoke appropriate tools to
generalize to a wider range of tasks that are beyond their capabilities. (3) Better Interpretability. Foundation
models are criticized for lacking transparency in their decision-making process (Linardatos et al., 2020), which
can be a signiﬁcant concern in applications such as healthcare or ﬁnance, where explainability is critical
Tool Learning with Foundation Models