LFC-DA: Logical Formula-Controlled Data Augmentation for Enhanced Logical Reasoning
This work addresses the challenge of enhancing logical reasoning in pretrained models for AI and NLP applications, though it is incremental as it builds on existing symbolic and generation methods.
The paper tackled the problem of costly human annotation and uninterpretable generation for logical data augmentation by introducing LFC-DA, a symbolic-logic-controlled pipeline that maps logical text to propositional expressions and uses bounded state-space search to generate diverse and rigorous natural-language questions, resulting in significant improvements in logical-reasoning accuracy on ReClor and LogiQA benchmarks.
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.