Large Language Models (LLMs) have exhibited remarkable potential across a wide array of reasoning tasks, including logical reasoning. Although massive efforts have been made to empower the logical reasoning ability of LLMs via external logical symbolic solvers, crucial challenges of the poor generalization ability to questions with different features and inevitable question information loss of symbolic solver-driven approaches remain unresolved. To mitigate these issues, we introduce LINA, a LLM-driven neuro-symbolic approach for faithful logical reasoning. By enabling an LLM to autonomously perform the transition from propositional logic extraction to sophisticated logical reasoning, LINA not only bolsters the resilience of the reasoning process but also eliminates the dependency on external solvers. Additionally, through its adoption of a hypothetical-deductive reasoning paradigm, LINA effectively circumvents the expansive search space challenge that plagues traditional forward reasoning methods. Empirical evaluations demonstrate that LINA substantially outperforms both established propositional logic frameworks and conventional prompting techniques across a spectrum of five logical reasoning tasks. Specifically, LINA achieves an improvement of 24.34% over LINC on the FOLIO dataset, while also surpassing prompting strategies like CoT and CoT-SC by up to 24.02%. Our code is available at https://github.com/wufeiwuwoshihua/nshy.

本研究解决了大型语言模型（LLMs）在逻辑推理中的泛化能力不足和信息损失问题。我们提出了一种名为LINA的神经符号方法，使LLM能够自主从命题逻辑提取过渡到复杂的逻辑推理，从而提高推理过程的稳健性，并消除对外部求解器的依赖。实验表明，LINA在五个逻辑推理任务中表现优于传统方法，其在FOLIO数据集上的性能提升达24.34%。

利用大型语言模型进行逻辑推理中的假设推断：一种神经符号方法