mlsanityData to 7 Oct 2026

Can neural networks understand monotonicity reasoning?

arXiv 1906.06448 ↗First posted 15 Jun 2019cs.CLcited by 45

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Abstract (arXiv)

Monotonicity reasoning is one of the important reasoning skills for any intelligent natural language inference (NLI) model in that it requires the ability to capture the interaction between lexical and syntactic structures. Since no test set has been developed for monotonicity reasoning with wide coverage, it is still unclear whether neural models can perform monotonicity reasoning in a proper way. To investigate this issue, we introduce the Monotonicity Entailment Dataset (MED). Performance by state-of-the-art NLI models on the new test set is substantially worse, under 55%, especially on downward reasoning. In addition, analysis using a monotonicity-driven data augmentation method showed that these models might be limited in their generalization ability in upward and downward reasoning.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jun 2019.

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Citing papers per year: 2021: 12; 2022: 8; 2023: 12; 2024: 7; 2025: 3; 2026: 3.

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  1. 2022FOLIO: Natural Language Reasoning with First-Order Logiccited by 233
  2. 2023Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4cited by 202
  3. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 97
  4. 2024LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Modelscited by 67
  5. 2023Natural Language Reasoning, A Surveycited by 61
  6. 2021Frequency Effects on Syntactic Rule Learning in Transformerscited by 59
  7. 2021NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoningcited by 24
  8. 2022Unit Testing for Concepts in Neural Networkscited by 23
  9. 2021Modeling Event Plausibility with Consistent Conceptual Abstractioncited by 20
  10. 2022Penguins Don't Fly: Reasoning about Generics through Instantiations and Exceptionscited by 17
  11. 2021Exploring Transitivity in Neural NLI Models through Veridicalitycited by 15
  12. 2021Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2cited by 15
33 more
  1. 2023Targeted Data Generation: Finding and Fixing Model Weaknessescited by 15
  2. 2023Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biasescited by 14
  3. 2024Semantic Sensitivities and Inconsistent Predictions: Measuring the Fragility of NLI Modelscited by 14
  4. 2024Exploring Reasoning Biases in Large Language Models Through Syllogism: Insights from the NeuBAROCO Datasetcited by 14
  5. 2023tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluationcited by 11
  6. 2024Scope Ambiguities in Large Language Modelscited by 11
  7. 2021Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logiccited by 9
  8. 2022Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inferencecited by 9
  9. 2022Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarkscited by 8
  10. 2021SyGNS: A Systematic Generalization Testbed Based on Natural Language Semanticscited by 7
  11. 2023Simple Linguistic Inferences of Large Language Models (LLMs): Blind Spots and Blindscited by 7
  12. 2023Evaluating Paraphrastic Robustness in Textual Entailment Modelscited by 7
  13. 2021Probing Linguistic Information For Logical Inference In Pre-trained Language Modelscited by 6
  14. 2025Thunder-NUBench: A Benchmark for LLMs' Sentence-Level Negation Understandingcited by 5
  15. 2021Decomposing Natural Logic Inferences in Neural NLIcited by 4
  16. 2023SpaceNLI: Evaluating the Consistency of Predicting Inferences in Spacecited by 4
  17. 2025LOGicalThought: Logic-Based Ontological Grounding of LLMs for High-Assurance Reasoningcited by 4
  18. 2022Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understandingcited by 3
  19. 2023Estimating the Causal Effects of Natural Logic Features in Neural NLI Modelscited by 3
  20. 2023NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logiccited by 3
  21. 2024Sneaking Syntax into Transformer Language Models with Tree Regularizationcited by 3
  22. 2021Attentive Tree-structured Network for Monotonicity Reasoningcited by 1
  23. 2023Explaining Interactions Between Text Spanscited by 1
  24. 2025Enigme: Generative Text Puzzles for Evaluating Reasoning in Language Modelscited by 1
  25. 2026The Grammar of Transformers: A Systematic Review of Interpretability Research on Syntactic Knowledge in Language Modelscited by 1
  26. 2026How Much Human Label Variation Does Formal Semantic Structure Explain? Group-Level Effects and Item-Level Ceilings in NLIcited by 1
  27. 2021Transformers in the loop: Polarity in neural models of languagecited by 0
  28. 2021A Logic-Based Framework for Natural Language Inference in Dutchcited by 0
  29. 2022Generating Intermediate Steps for NLI with Next-Step Supervisioncited by 0
  30. 2023Synthetic Dataset for Evaluating Complex Compositional Knowledge for Natural Language Inferencecited by 0
  31. 2024EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completioncited by 0
  32. 2024Estimating the Causal Effects of Natural Logic Features in Transformer-Based NLI Modelscited by 0
  33. 2026Selection Shapes the Boundary: A Preregistered Replication of Monotonicity and Label Agreement in Unselected NLI Populationscited by 0