LogiGAN: Learning Logical Reasoning via Adversarial Pre-training

• URL: http://arxiv.org/abs/2205.08794v1
• Date: Wed, 18 May 2022 08:46:49 GMT
• Title: LogiGAN: Learning Logical Reasoning via Adversarial Pre-training
• Authors: Xinyu Pi, Wanjun Zhong, Yan Gao, Nan Duan, Jian-Guang Lou
• Abstract summary: We present LogiGAN, an unsupervised adversarial pre-training framework for improving logical reasoning abilities of language models. Inspired by the facilitation effect of reflective thinking in human learning, we simulate the learning-thinking process with an adversarial Generator-Verifier architecture. Both base and large size language models pre-trained with LogiGAN demonstrate obvious performance improvement on 12 datasets.
• Score: 58.11043285534766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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