We study a synthetic corpus-based approach for language models (LMs) to acquire logical deductive reasoning ability. The previous studies generated deduction examples using specific sets of deduction rules. However, these rules were limited or otherwise arbitrary. This can limit the generalizability of acquired deductive reasoning ability. We rethink this and adopt a well-grounded set of deduction rules based on formal logic theory, which can derive any other deduction rules when combined in a multistep way. We empirically verify that LMs trained on the proposed corpora, which we name $\textbf{FLD}$ ($\textbf{F}$ormal $\textbf{L}$ogic $\textbf{D}$eduction), acquire more generalizable deductive reasoning ability. Furthermore, we identify the aspects of deductive reasoning ability on which deduction corpora can enhance LMs and those on which they cannot. Finally, on the basis of these results, we discuss the future directions for applying deduction corpora or other approaches for each aspect. We release the code, data, and models.

我们研究了一种基于合成语料库的方法来增强语言模型（LMs）的逻辑演绎推理能力。我们采用了基于形式逻辑理论的一组规则作为推论语料库，从而获得更具推广能力的演绎推理能力。此外，我们还确定了推论语料库对LMs可以提升的推理能力方面以及对其无法提升的方面，并讨论了应用推论语料库或其他方法在每个方面的未来发展方向。

基于形式逻辑的合成语料学习演绎推理