Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.09609 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929244214394880 |
|---|---|
| author | Liu, Zhexiong Zhang, Jing Lu, Jiaying Ma, Wenjing Ho, Joyce C |
| author_facet | Liu, Zhexiong Zhang, Jing Lu, Jiaying Ma, Wenjing Ho, Joyce C |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09609 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LogicPrpBank: A Corpus for Logical Implication and Equivalence Liu, Zhexiong Zhang, Jing Lu, Jiaying Ma, Wenjing Ho, Joyce C Computation and Language Artificial Intelligence 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. |
| title | LogicPrpBank: A Corpus for Logical Implication and Equivalence |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2402.09609 |