mlsanityData to 14 Oct 2026

Logic-Driven Context Extension and Data Augmentation for Logical Reasoning of Text

arXiv 2105.03659 ↗First posted 8 May 2021cs.CLcited by 46

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Cited by 33 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Logical reasoning of text requires understanding critical logical information in the text and performing inference over them. Large-scale pre-trained models for logical reasoning mainly focus on word-level semantics of text while struggling to capture symbolic logic. In this paper, we propose to understand logical symbols and expressions in the text to arrive at the answer. Based on such logical information, we not only put forward a context extension framework but also propose a data augmentation algorithm. The former extends the context to cover implicit logical expressions following logical equivalence laws. The latter augments literally similar but logically different instances to better capture logical information, especially logical negative and conditional relationships. We conduct experiments on ReClor dataset. The results show that our method achieves the state-of-the-art performance, and both logic-driven context extension framework and data augmentation algorithm can help improve the accuracy. And our multi-model ensemble system is the first to surpass human performance on both EASY set and HARD set of ReClor.

Key facts. Cited by 46 papers in the map (data through 14 Oct 2026). Builds on 32 of its 47 references that are in the map. First posted on arXiv 8 May 2021.

Builds on

32 of 47 references.

References · 32

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  4. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  5. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
  6. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  8. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  9. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  10. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,682
  11. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  12. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
20 more
  1. 2015A large annotated corpus for learning natural language inferencecited by 1,847
  2. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,845
  3. 2017Decoupled Weight Decay Regularizationcited by 1,831
  4. 2019DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphscited by 903
  5. 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 861
  6. 2020Keystroke Dynamics as Part of Lifeloggingcited by 334
  7. 2019Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoningcited by 285
  8. 2020ReClor: A Reading Comprehension Dataset Requiring Logical Reasoningcited by 217
  9. 2020Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learningcited by 216
  10. 2017Neural-Symbolic Learning and Reasoning: A Survey and Interpretationcited by 196
  11. 2019Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoningcited by 151
  12. 2019Hierarchical Graph Network for Multi-hop Question Answeringcited by 83
  13. 2019Neural Module Networks for Reasoning over Textcited by 66
  14. 2018Translating a Math Word Problem to an Expression Treecited by 57
  15. 2017The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrantscited by 56
  16. 2018Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examplescited by 34
  17. 2020Conversational Neuro-Symbolic Commonsense Reasoningcited by 28
  18. 2021DAGN: Discourse-Aware Graph Network for Logical Reasoningcited by 28
  19. 2020Natural Language Inference in Context -- Investigating Contextual Reasoning over Long Textscited by 23
  20. 2020Exploring Neural Models for Parsing Natural Language into First-Order Logiccited by 16

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 14; 2023: 15; 2024: 5; 2025: 6; 2026: 3.

46 papers.

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  1. 2022Large Language Models are Better Reasoners with Self-Verificationcited by 307
  2. 2022Making Large Language Models Better Reasoners with Step-Aware Verifiercited by 233
  3. 2023Active Prompting with Chain-of-Thought for Large Language Modelscited by 154
  4. 2023Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translationcited by 59
  5. 2023Large Language Models Are Not Strong Abstract Reasonerscited by 53
  6. 2025Empowering LLMs with Logical Reasoning: A Comprehensive Surveycited by 51
  7. 2022Reasoning Like Program Executorscited by 45
  8. 2021From LSAT: The Progress and Challenges of Complex Reasoningcited by 43
  9. 2024Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMscited by 28
  10. 2022CONE: An Efficient COarse-to-fiNE Alignment Framework for Long Video Temporal Groundingcited by 27
  11. 2024Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Taskscited by 23
  12. 2024Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Modelscited by 23
34 more
  1. 2022MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoningcited by 22
  2. 2025S2-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiencycited by 22
  3. 2023Chain of Thought Prompting Elicits Knowledge Augmentationcited by 21
  4. 2023GraphReason: Enhancing Reasoning Capabilities of Large Language Models through A Graph-Based Verification Approachcited by 21
  5. 2024Symbolic Working Memory Enhances Language Models for Complex Rule Applicationcited by 17
  6. 2025LazyReview A Dataset for Uncovering Lazy Thinking in NLP Peer Reviewscited by 13
  7. 2022LogiGAN: Learning Logical Reasoning via Adversarial Pre-trainingcited by 11
  8. 2023Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoningcited by 11
  9. 2023Assessing and Enhancing the Robustness of Large Language Models with Task Structure Variations for Logical Reasoningcited by 10
  10. 2021Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoningcited by 9
  11. 2022AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehensioncited by 8
  12. 2022Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoningcited by 8
  13. 2023Exploring Self-supervised Logic-enhanced Training for Large Language Modelscited by 8
  14. 2022KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Modelscited by 6
  15. 2022World Knowledge in Multiple Choice Reading Comprehensioncited by 6
  16. 2024Leveraging LLMs for Hypothetical Deduction in Logical Inference: A Neuro-Symbolic Approachcited by 6
  17. 2022APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoningcited by 5
  18. 2025Conflict-Aware Fusion: Mitigating Logic Inertia in Large Language Models via Structured Cognitive Priorscited by 5
  19. 2021Fact-driven Logical Reasoning for Machine Reading Comprehensioncited by 4
  20. 2023Analyzing Multiple-Choice Reading and Listening Comprehension Testscited by 4
  21. 2022MetaLogic: Logical Reasoning Explanations with Fine-Grained Structurecited by 3
  22. 2023IDOL: Indicator-oriented Logic Pre-training for Logical Reasoningcited by 3
  23. 2023Assessing Distractors in Multiple-Choice Testscited by 3
  24. 2022Discourse-Aware Graph Networks for Textual Logical Reasoningcited by 2
  25. 2023Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Textcited by 2
  26. 2026A Balanced Neuro-Symbolic Approach for Commonsense Abductive Logiccited by 2
  27. 2022BootAug: Boosting Text Augmentation via Hybrid Instance Filtering Frameworkcited by 1
  28. 2023Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehensioncited by 1
  29. 2025The Logical Implication Steering Method for Conditional Interventions on Transformer Generationcited by 1
  30. 2023Deep Manifold Learning for Reading Comprehension and Logical Reasoning Tasks with Polytuplet Losscited by 0
  31. 2023Proposition from the Perspective of Chinese Language: A Chinese Proposition Classification Evaluation Benchmarkcited by 0
  32. 2025LFC-DA: Logical Formula-Controlled Data Augmentation for Enhanced Logical Reasoningcited by 0
  33. 2026IFDNS: An Iterative Feedback-Driven Neuro-Symbolic Method for Faithful Logical Reasoningcited by 0
  34. 2026DualGuard: Dual-Mode Quality Control for Logic-Preserving Data Augmentationcited by 0