[MT(tortuga) → turtle] denotes the calling of a machine translation tool with tortuga being the input and
turtle being the output of the tool execution. The authors further demonstrate that with a few demonstrations,
foundation models can teach themselves how to utilize a tool in a self-supervised manner. Considering that
PLMs are typically pre-trained on textual data only, thus are limited in understanding and interacting with the
physical world, Liu et al. (2022) seek to bridge this gap and use a physics simulation engine (MuJoCo (Todorov
et al., 2012)) to make PLMs’ reasoning grounded to the real world. Experiments show that augmenting physics
simulation to PLMs could signiﬁcantly enhance their physical understanding and reasoning abilities. Chen
et al. (2022b); Gao et al. (2022) propose to augment PLMs with Python interpreters. Speciﬁcally, given
a complex task, PLMs ﬁrst understand it and generate programs as intermediate thoughts. After that, the
Tool Learning with Foundation Models