LogicInference: A new Datasaet for Teaching Logical Inference to seq2seq Models

Santiago Ontanon, Joshua Ainslie, Vaclav Cvicek, Zachary Fisher

Keywords: dataset, logical inference, reasoning, transformers

TL;DR: This paper presents a new sequence-to-sequence dataset called LogicInference, designed to teach logical inference to machine learning models.

Abstract: Machine learning models such as Transformers or LSTMs struggle with tasks that are compositional in nature such as those involving reasoning/inference. Although many datasets exist to evaluate compositional generalization, when it comes to evaluating inference abilities, options are more limited. This paper presents LogicInference, a new dataset to evaluate the ability of models to perform logical inference. The dataset focuses on inference using propositional logic and a small subset of first-order logic, represented both in semi-formal logical notation, as well as in natural language. We also report initial results using a collection of machine learning models to establish an initial baseline in this dataset.