  Reasoning about time is of fundamental importance. Many facts are
time-dependent. For example, athletes change teams from time to time, and
different government officials are elected periodically. Previous
time-dependent question answering (QA) datasets tend to be biased in either
their coverage of time spans or question types. In this paper, we introduce a
comprehensive probing dataset \tempreason to evaluate the temporal reasoning
capability of large language models. Our dataset includes questions of three
temporal reasoning levels. In addition, we also propose a novel learning
framework to improve the temporal reasoning capability of large language
models, based on temporal span extraction and time-sensitive reinforcement
learning. We conducted experiments in closed book QA, open book QA, and
reasoning QA settings and demonstrated the effectiveness of our approach. Our
code and data are released on https://github.com/DAMO-NLP-SG/TempReason.
