  We present LogiGAN, an unsupervised adversarial pre-training framework for
improving logical reasoning abilities of language models. Upon automatic
identifying logical reasoning phenomena in massive text corpus via detection
heuristics, we train language models to predict the masked-out logical
statements. Inspired by the facilitation effect of reflective thinking in human
learning, we analogically simulate the learning-thinking process with an
adversarial Generator-Verifier architecture to assist logic learning. LogiGAN
implements a novel sequential GAN approach that (a) circumvents the
non-differentiable challenge of the sequential GAN by leveraging the Generator
as a sentence-level generative likelihood scorer with a learning objective of
reaching scoring consensus with the Verifier; (b) is computationally feasible
for large-scale pre-training with arbitrary target length. Both base and large
size language models pre-trained with LogiGAN demonstrate obvious performance
improvement on 12 datasets requiring general reasoning abilities, revealing the
fundamental role of logic in broad reasoning, as well as the effectiveness of
LogiGAN. Ablation studies on LogiGAN components reveal the relative
orthogonality between linguistic and logic abilities and suggest that
reflective thinking's facilitation effect might also generalize to machine
learning.
