LogicInference: A New Dataset for Teaching Logical Inference to seq2seq Models
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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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Citing papers per year: 2023: 4; 2024: 3; 2025: 3; 2026: 2.
12 papers.
- 2023Evaluating Large Language Models: A Comprehensive Surveycited by 202
- 2023Natural Language Reasoning, A Surveycited by 61
- 2023LogiCoT: Logical Chain-of-Thought Instruction-Tuningcited by 31
- 2024Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanismcited by 15
- 2024Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understandingcited by 12
- 2025Reasoning Bias of Next Token Prediction Trainingcited by 3
- 2025Logical forms complement probability in understanding language model (and human) performancecited by 3
- 2025LogicLearner: A Tool for the Guided Practice of Propositional Logic Proofscited by 2
- 2023Transformers in the Service of Description Logic-based Contextscited by 0
- 2024LogicPrpBank: A Corpus for Logical Implication and Equivalencecited by 0
- 2026Stress-Testing the Reasoning Competence of LLMs With Proofs Under Minimal Formalismcited by 0
- 2026ChLogic: Evaluating Robustness of Logical Reasoning in Chinese Expressionscited by 0
- 2026ChLogic: Evaluating Robustness of Logical Reasoning in Chinese Expressions16 Jun 2026
- 2026Stress-Testing the Reasoning Competence of LLMs With Proofs Under Minimal Formalism7 Apr 2026
- 2025LogicLearner: A Tool for the Guided Practice of Propositional Logic Proofs25 Mar 2025
- 2025Logical forms complement probability in understanding language model (and human) performance13 Feb 2025
- 2025Reasoning Bias of Next Token Prediction Training4 Feb 2025
- 2024Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism24 May 2024
- 2024Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding4 Apr 2024
- 2024LogicPrpBank: A Corpus for Logical Implication and Equivalence14 Feb 2024
- 2023Transformers in the Service of Description Logic-based Contexts15 Nov 2023
- 2023Evaluating Large Language Models: A Comprehensive Survey30 Oct 2023
- 2023LogiCoT: Logical Chain-of-Thought Instruction-Tuning20 May 2023
- 2023Natural Language Reasoning, A Survey26 Mar 2023