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Probing Language Models for Understanding of Temporal Expressions

arXiv 2110.01113 ↗First posted 3 Oct 2021cs.CLcited by 16

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Abstract (arXiv)

We present three Natural Language Inference (NLI) challenge sets that can evaluate NLI models on their understanding of temporal expressions. More specifically, we probe these models for three temporal properties: (a) the order between points in time, (b) the duration between two points in time, (c) the relation between the magnitude of times specified in different units. We find that although large language models fine-tuned on MNLI have some basic perception of the order between points in time, at large, these models do not have a thorough understanding of the relation between temporal expressions.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 13 of its 27 references that are in the map. First posted on arXiv 3 Oct 2021.

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13 of 27 references.

References · 13

  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. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,682
  4. 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
  5. 2019Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inferencecited by 876
  6. 2020A Primer in BERTology: What we know about how BERT workscited by 815
  7. 2018Hypothesis Only Baselines in Natural Language Inferencecited by 295
  8. 2019Do NLP Models Know Numbers? Probing Numeracy in Embeddingscited by 159
  9. 2019"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understandingcited by 123
  10. 2020TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questionscited by 65
  11. 2019CogCompTime: A Tool for Understanding Time in Natural Language Textcited by 23
  12. 2019Fine-Grained Temporal Relation Extractioncited by 21
1 more
  1. 2018Temporal Information Extraction by Predicting Relative Time-linescited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 2; 2025: 4; 2026: 2.

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  1. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 130
  2. 2023Test of Time: Instilling Video-Language Models with a Sense of Timecited by 51
  3. 2024Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theorycited by 19
  4. 2024UnSeenTimeQA: Time-Sensitive Question-Answering Beyond LLMs' Memorizationcited by 17
  5. 2023Salient Span Masking for Temporal Understandingcited by 14
  6. 2025LexTime: A Benchmark for Temporal Ordering of Legal Eventscited by 10
  7. 2023Representation Of Lexical Stylistic Features In Language Models' Embedding Spacecited by 8
  8. 2025Around the World in 24 Hours: Probing LLM Knowledge of Time and Placecited by 7
  9. 2025Counterfactual-Consistency Prompting for Relative Temporal Understanding in Large Language Modelscited by 5
  10. 2023Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Modelscited by 4
  11. 2022Compositional Semantics and Inference System for Temporal Order based on Japanese CCGcited by 2
  12. 2023Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Modelscited by 2
4 more
  1. 2025Discrete Minds in a Continuous World: Do Language Models Know Time Passes?cited by 2
  2. 2023Towards Probing Contact Center Large Language Modelscited by 0
  3. 2026TempViz: On the Evaluation of Temporal Knowledge in Text-to-Image Modelscited by 0
  4. 2026A Study of Temporal Fusion Strategies for Named Entity Recognition in Historical Textscited by 0