Transformers Generalize to the Semantics of Logics
Christopher Hahn, Frederik Schmitt, Jens U. Kreber, Markus N. Rabe, Bernd Finkbeiner
We show that neural networks can learn the semantics of propositional and linear-time temporal logic (LTL) from imperfect training data. Instead of only predicting the truth value of a formula, we use a Transformer architecture to predict the solution for a given formula, e.g., a variable assignment for a formula in propositional logic. Most formulas have many solutions and the training data thus depends on the particularities of the generator. We make the surprising observation that while the Transformer does not perfectly predict the generator’s output, it still produces correct solutions to almost all formulas, even when its prediction deviates from the generator. It appears that it is easier to learn the semantics of the logics than the particularities of the generator. We observe that the Transformer preserves this semantic generalization even when challenged with formulas of a size it has never encountered before. Surprisingly, the Transformer solves almost all LTL formulas in our test set including those for which our generator timed out.