LFC-DA: Logical Formula-Controlled Data Augmentation for Enhanced Logical Reasoning

• URL: http://arxiv.org/abs/2511.03372v1
• Date: Wed, 05 Nov 2025 11:26:38 GMT
• Title: LFC-DA: Logical Formula-Controlled Data Augmentation for Enhanced Logical Reasoning
• Authors: Shenghao Li,
• Abstract summary: LFC-DA is a symbolic-logic-controlled pipeline for complex logical data augmentation.<n>It maps logical text to propositional expressions, a compact rule library is compiled, and a bounded state-space search systematically discovers valid formulas.<n>Experiments on ReClor and LogiQA show significant improvements in the logical-reasoning accuracy of pretrained models.
• Score: 3.553493344868413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For complex logical data augmentation, heavy reliance on human annotation is costly, whereas direct generation with large language models yields uninterpretable and logically homogeneous examples. To address this, we present LFC-DA, a symbolic-logic-controlled pipeline: logical text is first mapped to propositional expressions, a compact rule library is compiled, and a bounded state-space search systematically discovers valid formulas that are then verbalized back into natural-language questions, ensuring both diversity and logical rigor under propositional logic. Experiments on ReClor and LogiQA show significant improvements in the logical-reasoning accuracy of pretrained models, confirming the effectiveness of LFC-DA for LLM-guided logical data augmentation.

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