  Generating coherent and credible explanations remains a significant challenge
in the field of AI. In recent years, researchers have delved into the
utilization of entailment trees to depict explanations, which exhibit a
reasoning process of how a hypothesis is deduced from the supporting facts.
However, existing models often overlook the importance of generating
intermediate conclusions with logical consistency from the given facts, leading
to inaccurate conclusions and undermining the overall credibility of entailment
trees. To address this limitation, we propose the logical pattern memory
pre-trained model (LMPM). LMPM incorporates an external memory structure to
learn and store the latent representations of logical patterns, which aids in
generating logically consistent conclusions. Furthermore, to mitigate the
influence of logically irrelevant domain knowledge in the Wikipedia-based data,
we introduce an entity abstraction approach to construct the dataset for
pre-training LMPM. The experimental results highlight the effectiveness of our
approach in improving the quality of entailment tree generation. By leveraging
logical entailment patterns, our model produces more coherent and reasonable
conclusions that closely align with the underlying premises. Code and Data are
released at https://github.com/YuanLi95/T5-LMPM
