Summary of Logicprpbank: a Corpus For Logical Implication and Equivalence, by Zhexiong Liu et al.
LogicPrpBank: A Corpus for Logical Implication and Equivalence
by Zhexiong Liu, Jing Zhang, Jiaying Lu, Wenjing Ma, Joyce C Ho
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers aim to improve the ability of Language Models (LMs) to reason complex mathematical problems using propositional logic. While LMs have shown capabilities in handling various reasoning tasks, their performance on propositional logic remains largely unexplored due to limited annotated corpora. To address this gap, the authors present a new corpus, LogicPrpBank, containing 7093 Propositional Logic Statements (PLSs) across six mathematical subjects. This corpus is benchmarked with widely-used LMs, demonstrating its potential as a valuable resource for improving model performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Logic reasoning is crucial in problem-solving and decision-making. Researchers have created a new dataset called LogicPrpBank to help Language Models get better at solving complex math problems using logic rules. The dataset has 7093 examples of logical statements from six different math subjects. This will help improve the models’ ability to reason about implications and equivalencies. |
