Pre-trained language models (LMs) are able to perform complex reasoning without explicit fine-tuning. To understand how pre-training with a next-token prediction objective contributes to the emergence of such reasoning capability, we propose that we can view an LM as deriving new conclusions by aggregating indirect reasoning paths seen at pre-training time. We found this perspective effective in two important cases of reasoning: logic reasoning with knowledge graphs (KGs) and math reasoning with math word problems (MWPs). More specifically, we formalize the reasoning paths as random walk paths on the knowledge/reasoning graphs. Analyses of learned LM distributions suggest that a weighted sum of relevant random walk path probabilities is a reasonable way to explain how LMs reason. Experiments and analysis on multiple KG and MWP datasets reveal the effect of training on random walk paths and suggest that augmenting unlabeled random walk reasoning paths can improve real-world multi-step reasoning performance.

使用预训练语言模型进行复杂推理，理解预训练中的下一标记预测贡献于推理能力，透过聚合间接推理路径来产生新结论，在逻辑推理和数学推理中通过随机游走路径对知识图谱和数学问题中的推理过程进行建模，研究表明，加强未标记的随机游走推理路径可以提高现实世界中的多步推理性能。

从推理路径聚合的角度理解语言模型的推理能力