mlsanityData to 14 Oct 2026

"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding

arXiv 1909.03065 ↗First posted 6 Sep 2019cs.CLcited by 123

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Cited by 30 in LLM reasoning, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Understanding time is crucial for understanding events expressed in natural language. Because people rarely say the obvious, it is often necessary to have commonsense knowledge about various temporal aspects of events, such as duration, frequency, and temporal order. However, this important problem has so far received limited attention. This paper systematically studies this temporal commonsense problem. Specifically, we define five classes of temporal commonsense, and use crowdsourcing to develop a new dataset, MCTACO, that serves as a test set for this task. We find that the best current methods used on MCTACO are still far behind human performance, by about 20%, and discuss several directions for improvement. We hope that the new dataset and our study here can foster more future research on this topic.

Key facts. Cited by 123 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Sep 2019.

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Citing papers per year: 2020: 1; 2021: 23; 2022: 24; 2023: 31; 2024: 17; 2025: 21; 2026: 6.

123 papers.

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  1. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 843
  2. 2020Aligning AI With Shared Human Valuescited by 723
  3. 2021Cross-Task Generalization via Natural Language Crowdsourcing Instructionscited by 699
  4. 2024Scaling and evaluating sparse autoencoderscited by 553
  5. 2023Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervisioncited by 467
  6. 2021MetaICL: Learning to Learn In Contextcited by 454
  7. 2021Time-Aware Language Models as Temporal Knowledge Basescited by 296
  8. 2024Continual Learning of Large Language Models: A Comprehensive Surveycited by 234
  9. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  10. 2021Reframing Instructional Prompts to GPTk's Languagecited by 176
  11. 2023Large-scale Multi-Modal Pre-trained Models: A Comprehensive Surveycited by 153
  12. 2021CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLPcited by 142
38 more
  1. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 130
  2. 2022A Survey of Deep Learning for Mathematical Reasoningcited by 124
  3. 2025Are Sparse Autoencoders Useful? A Case Study in Sparse Probingcited by 115
  4. 2024Large Language Models Can Learn Temporal Reasoningcited by 111
  5. 2025Does Math Reasoning Improve General LLM Capabilities? Understanding Transferability of LLM Reasoningcited by 106
  6. 2022What Language Model to Train if You Have One Million GPU Hours?cited by 92
  7. 2021QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehensioncited by 91
  8. 2023SpokenWOZ: A Large-Scale Speech-Text Benchmark for Spoken Task-Oriented Dialogue Agentscited by 71
  9. 2024LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Modelscited by 68
  10. 2023ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Modelscited by 63
  11. 2021Broaden the Vision: Geo-Diverse Visual Commonsense Reasoningcited by 59
  12. 2021Comparing Test Sets with Item Response Theorycited by 57
  13. 2022Improving In-Context Few-Shot Learning via Self-Supervised Trainingcited by 57
  14. 2022Housekeep: Tidying Virtual Households using Commonsense Reasoningcited by 57
  15. 2021TIMEDIAL: Temporal Commonsense Reasoning in Dialogcited by 55
  16. 2021AR-LSAT: Investigating Analytical Reasoning of Textcited by 51
  17. 2023Test of Time: Instilling Video-Language Models with a Sense of Timecited by 51
  18. 2022Don't Blame the Annotator: Bias Already Starts in the Annotation Instructionscited by 47
  19. 2021From LSAT: The Progress and Challenges of Complex Reasoningcited by 43
  20. 2021COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentencescited by 40
  21. 2023Benchmarks for Automated Commonsense Reasoning: A Surveycited by 40
  22. 2023TRAM: Benchmarking Temporal Reasoning for Large Language Modelscited by 40
  23. 2022Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Surveycited by 38
  24. 2021Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schemacited by 32
  25. 2021ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoningcited by 30
  26. 2022A Survey of Knowledge-Intensive NLP with Pre-Trained Language Modelscited by 30
  27. 2021DAGN: Discourse-Aware Graph Network for Logical Reasoningcited by 28
  28. 2025Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarkscited by 28
  29. 2023Are Large Language Models Temporally Grounded?cited by 27
  30. 2021Exploring Universal Intrinsic Task Subspace via Prompt Tuningcited by 25
  31. 2021LOME: Large Ontology Multilingual Extractioncited by 23
  32. 2024When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Modelscited by 22
  33. 2023Mitigating Temporal Misalignment by Discarding Outdated Factscited by 21
  34. 2024Weak-to-Strong Generalization Through the Data-Centric Lenscited by 21
  35. 2021Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understandingcited by 19
  36. 2022Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Textscited by 18
  37. 2024UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorizationcited by 17
  38. 2021Probing Language Models for Understanding of Temporal Expressionscited by 16

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