mlsanityData to 7 Oct 2026

Abductive Commonsense Reasoning

arXiv 1908.05739 ↗First posted 15 Aug 2019cs.CLcited by 300

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Cited by 93 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation. While abduction has long been considered to be at the core of how people interpret and read between the lines in natural language (Hobbs et al., 1988), there has been relatively little research in support of abductive natural language inference and generation. We present the first study that investigates the viability of language-based abductive reasoning. We introduce a challenge dataset, ART, that consists of over 20k commonsense narrative contexts and 200k explanations. Based on this dataset, we conceptualize two new tasks -- (i) Abductive NLI: a multiple-choice question answering task for choosing the more likely explanation, and (ii) Abductive NLG: a conditional generation task for explaining given observations in natural language. On Abductive NLI, the best model achieves 68.9% accuracy, well below human performance of 91.4%. On Abductive NLG, the current best language generators struggle even more, as they lack reasoning capabilities that are trivial for humans. Our analysis leads to new insights into the types of reasoning that deep pre-trained language models fail to perform--despite their strong performance on the related but more narrowly defined task of entailment NLI--pointing to interesting avenues for future research.

Key facts. Cited by 300 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Aug 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 59; 2022: 65; 2023: 70; 2024: 43; 2025: 28; 2026: 35.

300 papers.

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  1. 2022Diffusion-LM Improves Controllable Text Generationcited by 1,006
  2. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
  3. 2021MetaICL: Learning to Learn In Contextcited by 450
  4. 2022Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thoughtcited by 334
  5. 2022Large Language Models are Better Reasoners with Self-Verificationcited by 304
  6. 2022UL2: Unifying Language Learning Paradigmscited by 292
  7. 2021SPoT: Better Frozen Model Adaptation through Soft Prompt Transfercited by 235
  8. 2022Making Large Language Models Better Reasoners with Step-Aware Verifiercited by 231
  9. 2021Generated Knowledge Prompting for Commonsense Reasoningcited by 213
  10. 2022Leveraging Large Language Models for Multiple Choice Question Answeringcited by 199
  11. 2023FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Setscited by 198
  12. 2023Large Language Models for Generative Information Extraction: A Surveycited by 183
38 more
  1. 2021ExT5: Towards Extreme Multi-Task Scaling for Transfer Learningcited by 161
  2. 2022COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamicscited by 159
  3. 2022Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanationscited by 159
  4. 2022A Review on Language Models as Knowledge Basescited by 156
  5. 2022Deep Bidirectional Language-Knowledge Graph Pretrainingcited by 156
  6. 2023Active Prompting with Chain-of-Thought for Large Language Modelscited by 154
  7. 2023Large-scale Multi-Modal Pre-trained Models: A Comprehensive Surveycited by 153
  8. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  9. 2023Can Large Language Models Infer Causation from Correlation?cited by 142
  10. 2021Can Machines Learn Morality? The Delphi Experimentcited by 140
  11. 2024Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoningcited by 136
  12. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  13. 2023CLadder: Assessing Causal Reasoning in Language Modelscited by 102
  14. 2021UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmarkcited by 98
  15. 2023The Generative AI Paradox: "What It Can Create, It May Not Understand"cited by 97
  16. 2023Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debatecited by 94
  17. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  18. 2024The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Modelscited by 89
  19. 2021e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Taskscited by 84
  20. 2025Tarsier2: Advancing Large Vision-Language Models from Detailed Video Description to Comprehensive Video Understandingcited by 80
  21. 2023Cognitive Reframing of Negative Thoughts through Human-Language Model Interactioncited by 77
  22. 2022A Survey of Knowledge Enhanced Pre-trained Language Modelscited by 76
  23. 2021Probing Across Time: What Does RoBERTa Know and When?cited by 73
  24. 2022Stop Measuring Calibration When Humans Disagreecited by 70
  25. 2024Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?cited by 68
  26. 2024MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypothesescited by 68
  27. 2023Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyondcited by 65
  28. 2021What to Pre-Train on? Efficient Intermediate Task Selectioncited by 62
  29. 2023Natural Language Reasoning, A Surveycited by 61
  30. 2024Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Datacited by 61
  31. 2021RiddleSense: Reasoning about Riddle Questions Featuring Linguistic Creativity and Commonsense Knowledgecited by 60
  32. 2021The Web Is Your Oyster - Knowledge-Intensive NLP against a Very Large Web Corpuscited by 60
  33. 2021Broaden the Vision: Geo-Diverse Visual Commonsense Reasoningcited by 59
  34. 2021ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoningcited by 58
  35. 2022minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Modelscited by 58
  36. 2022Housekeep: Tidying Virtual Households using Commonsense Reasoningcited by 57
  37. 2021A Systematic Investigation of Commonsense Knowledge in Large Language Modelscited by 56
  38. 2021Comparing Test Sets with Item Response Theorycited by 55

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