Recent research on transformer-based language models investigates their reasoning ability over logical rules expressed in natural language text. However, their logic is not yet well-understood as we cannot explain the abstractions made by the models that help them in reasoning. These models are criticized for merely memorizing complex patterns in the data, which often creates issues for their generalizability in unobserved situations. In this work, we analyze the use of probabilistic logical rules in transformer-based language models. In particular, we propose a new approach, Probabilistic Constraint Training (PCT), that explicitly models probabilistic logical reasoning by imposing the rules of reasoning as constraints during training. We create a new QA benchmark for evaluating probabilistic reasoning over uncertain textual rules, which creates instance-specific rules, unlike the only existing relevant benchmark. Experimental results show that our proposed technique improves the base language models' accuracy and explainability when probabilistic logical reasoning is required for question answering. Moreover, we show that the learned probabilistic reasoning abilities are transferable to novel situations.

本研究分析了transformer-based语言模型中的概率逻辑规则应用，并提出了Probabilistic Constraint Training (PCT)方法，该方法显式地模拟了概率逻辑推理，并在新的QA基准中评估了其准确性和可解释性。结果表明，该方法可以提高基本语言模型在需要概率逻辑推理时的准确性和解释性，并且该技术的学习也可适用于新的情境。

教授Transformer概率逻辑推理