Google Research

LogicInference: A New Dataset for Teaching Logical Inference to seq2seq Models

  • Santiago Ontanon
  • Joshua Ainslie
  • Vaclav Cvicek
  • Zach Fisher
ICLR 2022 Workshop on Elements of Reasoning: Objects, Structure and Causality


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.

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