mlsanityData to 14 Oct 2026

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

arXiv 2203.15099 ↗First posted 28 Mar 2022cs.AIcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 15 of its 25 references that are in the map. First posted on arXiv 28 Mar 2022.

Builds on

15 of 25 references.

References · 15

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3 more
  1. 2020Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architecturescited by 95
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 3; 2026: 2.

12 papers.