LogicPrpBank: A Corpus for Logical Implication and Equivalence

Zhexiong Liu, Jing Zhang, Jiaying Lu, Wenjing Ma, Joyce C. Ho

Track: Innovations in AI for Education (Day 1)

Paper Length: long-paper (6 pages + references)

Keywords: Propositional Logic, Logical Reasoning, Language Models, Few-shot Learning

Abstract: 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.

Submission Number: 43