mlsanityData to 14 Oct 2026

Explain Yourself! Leveraging Language Models for Commonsense Reasoning

arXiv 1906.02361 ↗First posted 6 Jun 2019cs.CLcited by 367

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Cited by 91 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep learning models perform poorly on tasks that require commonsense reasoning, which often necessitates some form of world-knowledge or reasoning over information not immediately present in the input. We collect human explanations for commonsense reasoning in the form of natural language sequences and highlighted annotations in a new dataset called Common Sense Explanations (CoS-E). We use CoS-E to train language models to automatically generate explanations that can be used during training and inference in a novel Commonsense Auto-Generated Explanation (CAGE) framework. CAGE improves the state-of-the-art by 10% on the challenging CommonsenseQA task. We further study commonsense reasoning in DNNs using both human and auto-generated explanations including transfer to out-of-domain tasks. Empirical results indicate that we can effectively leverage language models for commonsense reasoning.

Key facts. Cited by 367 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Jun 2019.

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Citing papers per year: 2021: 60; 2022: 86; 2023: 90; 2024: 70; 2025: 47; 2026: 14.

367 papers.

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  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  3. 2022Scaling Instruction-Finetuned Language Modelscited by 3,024
  4. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  5. 2023Improving Factuality and Reasoning in Language Models through Multiagent Debatecited by 1,784
  6. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,546
  7. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,202
  8. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 940
  9. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 737
  10. 2022Towards Reasoning in Large Language Models: A Surveycited by 596
  11. 2023Explainability for Large Language Models: A Surveycited by 333
  12. 2022From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AIcited by 302
38 more
  1. 2024Quiet-STaR: Language Models Can Teach Themselves to Think Before Speakingcited by 301
  2. 2021Recursively Summarizing Books with Human Feedbackcited by 290
  3. 2022Can language models learn from explanations in context?cited by 249
  4. 2022OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalizationcited by 236
  5. 2023Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Drivingcited by 232
  6. 2021Generated Knowledge Prompting for Commonsense Reasoningcited by 214
  7. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  8. 2022Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Modelscited by 180
  9. 2022The Unreliability of Explanations in Few-shot Prompting for Textual Reasoningcited by 169
  10. 2022Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanationscited by 159
  11. 2022Distilling Reasoning Capabilities into Smaller Language Modelscited by 158
  12. 2021Post-hoc Interpretability for Neural NLP: A Surveycited by 156
  13. 2021Explaining Answers with Entailment Treescited by 152
  14. 2023Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Stepcited by 148
  15. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
  16. 2022Natural Language Descriptions of Deep Visual Featurescited by 137
  17. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  18. 2024Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoningcited by 136
  19. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 130
  20. 2023Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulnesscited by 130
  21. 2022Text and Patterns: For Effective Chain of Thought, It Takes Two to Tangocited by 128
  22. 2021Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processingcited by 126
  23. 2021Reframing Human-AI Collaboration for Generating Free-Text Explanationscited by 125
  24. 2022Explanations from Large Language Models Make Small Reasoners Bettercited by 124
  25. 2023Teaching Arithmetic to Small Transformerscited by 123
  26. 2023Hypothesis Search: Inductive Reasoning with Language Modelscited by 120
  27. 2022Rationale-Augmented Ensembles in Language Modelscited by 111
  28. 2024Rethinking Interpretability in the Era of Large Language Modelscited by 111
  29. 2025A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systemscited by 111
  30. 2023Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Modelscited by 110
  31. 2023Faithfulness Tests for Natural Language Explanationscited by 107
  32. 2023SCOTT: Self-Consistent Chain-of-Thought Distillationcited by 105
  33. 2024SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationalescited by 105
  34. 2024STaR-GATE: Teaching Language Models to Ask Clarifying Questionscited by 104
  35. 2024Are self-explanations from Large Language Models faithful?cited by 101
  36. 2024InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationalescited by 101
  37. 2022Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanationscited by 85
  38. 2021e-ViL: A Dataset and Benchmark for Natural Language Explanations in Vision-Language Taskscited by 84

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