  Understanding what constitutes safe text is an important issue in natural
language processing and can often prevent the deployment of models deemed
harmful and unsafe. One such type of safety that has been scarcely studied is
commonsense physical safety, i.e. text that is not explicitly violent and
requires additional commonsense knowledge to comprehend that it leads to
physical harm. We create the first benchmark dataset, SafeText, comprising
real-life scenarios with paired safe and physically unsafe pieces of advice. We
utilize SafeText to empirically study commonsense physical safety across
various models designed for text generation and commonsense reasoning tasks. We
find that state-of-the-art large language models are susceptible to the
generation of unsafe text and have difficulty rejecting unsafe advice. As a
result, we argue for further studies of safety and the assessment of
commonsense physical safety in models before release.
