Results. We present the results in Table 2, from which we can conclude that: (1) In most cases, models
can learn how to effectively use tools with simple prompting, and improve their task performances. (2) For
the tasks that models can leverage their internal knowledge to solve (such as the cases of the calculator and
search engine), utilizing tools with zero-shot prompting could sometimes lead to worse performance, which
implies that sub-optimal utilization of tools may negatively impact performance. Nevertheless, incorporating
tools with few-shot prompting still consistently yields superior performance than not incorporating tools. This
underscores the concrete beneﬁts that tools can bring to problem-solving, provided that they are employed
effectively. (3) Additionally, comparing the performance of ChatGPT and text-davinci-003, we observe that
although ChatGPT has been ﬁne-tuned with RLHF, it does not yield better results than text-davinci-003. We
Tool Learning with Foundation Models