LFC-DA：用于增强逻辑推理的逻辑公式控制数据增强
LFC-DA: Logical Formula-Controlled Data Augmentation for Enhanced Logical Reasoning
AI总结：
本文提出LFC-DA流水线，通过符号逻辑公式控制数据增强，在ReClor和LogiQA上显著提升预训练模型的逻辑推理准确率。
摘要：
对于复杂的逻辑数据增强，严重依赖人工标注成本高昂，而使用大语言模型直接生成则会产生不可解释且逻辑同质化的示例。为解决这一问题，我们提出LFC-DA，一种符号逻辑控制的流水线：首先将逻辑文本映射为命题表达式，编译一个紧凑的规则库，然后通过有界状态空间搜索系统地发现有效公式，再将其语言化回自然语言问题，从而在命题逻辑下同时保证多样性和逻辑严谨性。在ReClor和LogiQA上的实验表明，预训练模型的逻辑推理准确率显著提升，证实了LFC-DA在LLM引导的逻辑数据增强方面的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
