Logical reasoning task has attracted great interest since it was proposed. Faced with such a task, current competitive models, even large language models (e.g., ChatGPT and PaLM 2), still perform badly. Previous promising LMs struggle in logical consistency modeling and logical structure perception. To this end, we model the logical reasoning task by transforming each logical sample into reasoning paths and propose an architecture \textbf{PathReasoner}. It addresses the task from the views of both data and model. To expand the diversity of the logical samples, we propose an atom extension strategy supported by equivalent logical formulas, to form new reasoning paths. From the model perspective, we design a stack of transformer-style blocks. In particular, we propose a path-attention module to joint model in-atom and cross-atom relations with the high-order diffusion strategy. Experiments show that PathReasoner achieves competitive performances on two logical reasoning benchmarks and great generalization abilities.

通过将每个逻辑样本转化为推理路径并提出PathReasoner架构，从数据和模型的角度探索逻辑推理任务，增加逻辑样本多样性并设计一种变压器式块堆叠，并采用路径注意力模块来对高阶传播策略进行内外原子关系建模，实验证明PathReasoner在两个逻辑推理基准测试上具有可比较的性能和出色的泛化能力。

PathReasoner: 以等效扩展模拟推理路径的逻辑问题解答