  We present three Natural Language Inference (NLI) challenge sets that can
evaluate NLI models on their understanding of temporal expressions. More
specifically, we probe these models for three temporal properties: (a) the
order between points in time, (b) the duration between two points in time, (c)
the relation between the magnitude of times specified in different units. We
find that although large language models fine-tuned on MNLI have some basic
perception of the order between points in time, at large, these models do not
have a thorough understanding of the relation between temporal expressions.
