# ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events

arXiv: https://arxiv.org/abs/2501.03040 · HTML: https://mlsanity.com/paper/2501.03040/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 6 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Time-Aware Language Models as Temporal Knowledge Bases](https://mlsanity.com/paper/2106.15110/) (2021)
- [A Dataset for Answering Time-Sensitive Questions](https://mlsanity.com/paper/2108.06314/) (2021)
- [Large Language Models Can Learn Temporal Reasoning](https://mlsanity.com/paper/2401.06853/) (2024)
- [Question Answering Over Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.01515/) (2021)
- [Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning](https://mlsanity.com/paper/2406.09170/) (2024)
- [Complex Temporal Question Answering on Knowledge Graphs](https://mlsanity.com/paper/2109.08935/) (2021)
- [TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions](https://mlsanity.com/paper/2005.00242/) (2020)
- [Temporal Reasoning on Implicit Events from Distant Supervision](https://mlsanity.com/paper/2010.12753/) (2020)
- [TEQUILA: Temporal Question Answering over Knowledge Bases](https://mlsanity.com/paper/1908.03650/) (2019)
- [Logical Reasoning over Natural Language as Knowledge Representation: A Survey](https://mlsanity.com/paper/2303.12023/) (2023)
- [Allen's Interval Algebra Makes the Difference](https://mlsanity.com/paper/1909.01128/) (2019)

## Built on by (most cited first, 18 of 18)

- [TCP: a Benchmark for Temporal Constraint-Based Planning](https://mlsanity.com/paper/2505.19927/) (2025)
- [When Silence Is Golden: Can LLMs Learn to Abstain in Temporal QA and Beyond?](https://mlsanity.com/paper/2602.04755/) (2026)
- [Position: Foundation Models for Tabular Data within Systemic Contexts Need Grounding](https://mlsanity.com/paper/2505.19825/) (2025)
- [Efficient Temporal-aware Matryoshka Adaptation for Temporal Information Retrieval](https://mlsanity.com/paper/2601.05549/) (2026)
- [Reasoning Gets Harder for LLMs Inside A Dialogue](https://mlsanity.com/paper/2603.20133/) (2026)
- [Rethinking LLM-Based RTL Code Optimization Via Timing Logic Metamorphosis](https://mlsanity.com/paper/2507.16808/) (2025)
- [Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning](https://mlsanity.com/paper/2509.00975/) (2025)
- [HistoryBankQA: Multilingual Temporal Question Answering on Historical Events](https://mlsanity.com/paper/2509.12720/) (2025)
- [NeSTR: A Neuro-Symbolic Abductive Framework for Temporal Reasoning in Large Language Models](https://mlsanity.com/paper/2512.07218/) (2025)
- [What Really Controls Temporal Reasoning in Large Language Models: Tokenisation or Representation of Time?](https://mlsanity.com/paper/2603.19017/) (2026)
- [Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties](https://mlsanity.com/paper/2502.16922/) (2025)
- [Expertise Is What We Want](https://mlsanity.com/paper/2502.20335/) (2025)
- [Harnessing Temporal Databases for Systematic Evaluation of Factual Time-Sensitive Question-Answering in Large Language Models](https://mlsanity.com/paper/2508.02045/) (2025)
- [Time to Revist Exact Match](https://mlsanity.com/paper/2509.16720/) (2025)
- [Structured yet Bounded Temporal Understanding in Large Language Models](https://mlsanity.com/paper/2510.16685/) (2025)
- [LiFT: How to Enable In-Context Learning for Longitudinal Modelling](https://mlsanity.com/paper/2604.16382/) (2026)
- [Uncovering Temporal Framing in the News](https://mlsanity.com/paper/2606.00294/) (2026)
- [Evaluating Temporal Reasoning in Large Language Models: Event Temporal Relation Extraction with Allen's Interval Algebra, Logical Consistency, and Relative Time in Narratives](https://mlsanity.com/paper/2610.17402/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have achieved remarkable success in various NLP tasks, yet they still face significant challenges in reasoning and arithmetic. Temporal reasoning, a critical component of natural language understanding, has raised increasing research attention. However, comprehensive testing of Allen's interval relations (e.g., before, after, during) -- a fundamental framework for temporal relationships -- remains underexplored. To fill this gap, we present ChronoSense, a new benchmark for evaluating LLMs' temporal understanding. It includes 16 tasks, focusing on identifying the Allen relation between two temporal events and temporal arithmetic, using both abstract events and real-world data from Wikidata. We assess the performance of seven recent LLMs using this benchmark and the results indicate that models handle Allen relations, even symmetrical ones, quite differently. Moreover, the findings suggest that the models may rely on memorization to answer time-related questions. Overall, the models' low performance highlights the need for improved temporal understanding in LLMs and ChronoSense offers a robust framework for future research in this area. Our dataset and the source code are available at https://github.com/duyguislakoglu/chronosense.