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 FLD
(Formal Logic Deduction), 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