Logical reasoning remains a challenge for natural language processing, but it
can be improved by training language models to mimic theorem provers on
procedurally generated problems. Previous work used domain-specific proof
generation algorithms, which biases reasoning toward specific proof traces and
limits auditability and extensibility. We present a simpler and more general
declarative framework with flexible context-sensitive rules binding multiple
languages (specifically, simplified English and the TPTP theorem-proving
language). We construct first-order logic problems by selecting up to 32
premises and one hypothesis. We demonstrate that using semantic constraints
during generation and careful English verbalization of predicates enhances
logical reasoning without hurting natural English tasks. We use relatively
small DeBERTa-v3 models to achieve state-of-the-art accuracy on the FOLIO
human-authored logic dataset, surpassing GPT-4 in accuracy with or without an
external solver by 12%.

利用培训语言模型模仿定理证明器在过程生成的问题上进行逻辑推理仍然是自然语言处理的一个挑战。我们提出了一个更简单、更普遍的声明性框架，通过灵活的上下文敏感规则来绑定多种语言（特别是简化英语和 TPTP 定理证明语言），从而改进了逻辑推理的能力。我们使用语义约束在生成过程中，并对谓词进行仔细的英语表达，提高了逻辑推理能力，而不损害自然英语任务。我们使用相对较小的 DeBERTa-v3 模型在人类编写的逻辑数据集 FOLIO 上实现了最先进的准确性，在有或无外部求解器的情况下，精度超过了 GPT-4 12%。