A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models. To tackle this problem, we propose an approach that relies on training with synthetic theorems, generated from a set of axioms. We show that such theorems can be used to train an automated prover and that the learned prover transfers successfully to human-generated theorems. We demonstrate that a prover trained exclusively on synthetic theorems can solve a substantial fraction of problems in TPTP, a benchmark dataset that is used to compare state-of-the-art heuristic provers. Our approach outperforms a model trained on human-generated problems in most axiom sets, thereby showing the promise of using synthetic data for this task.

中文翻译：

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学习从综合定理证明

将机器学习应用于自动定理证明的一个主要挑战是训练数据的稀缺性，这是训练成功的深度学习模型的关键因素。为了解决这个问题，我们提出了一种方法，该方法依赖于从一组公理生成的合成定理的训练。我们表明，此类定理可用于训练自动证明者，并且学习到的证明者成功地转移到人工生成的定理。我们证明了专门接受合成定理训练的证明者可以解决 TPTP 中的大部分问题，TPTP 是一个基准数据集，用于比较最先进的启发式证明者。我们的方法优于在大多数公理集中针对人为问题训练的模型，从而显示了在此任务中使用合成数据的前景。