  Logic reasoning has been critically needed in problem-solving and
decision-making. Although Language Models (LMs) have demonstrated capabilities
of handling multiple reasoning tasks (e.g., commonsense reasoning), their
ability to reason complex mathematical problems, specifically propositional
logic, remains largely underexplored. This lack of exploration can be
attributed to the limited availability of annotated corpora. Here, we present a
well-labeled propositional logic corpus, LogicPrpBank, containing 7093
Propositional Logic Statements (PLSs) across six mathematical subjects, to
study a brand-new task of reasoning logical implication and equivalence. We
benchmark LogicPrpBank with widely-used LMs to show that our corpus offers a
useful resource for this challenging task and there is ample room for model
improvement.
