# Are Large Language Models Temporally Grounded?

arXiv: https://arxiv.org/abs/2311.08398 · HTML: https://mlsanity.com/paper/2311.08398/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 26 of 26)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [Recurrent World Models Facilitate Policy Evolution](https://mlsanity.com/paper/1809.01999/) (2018)
- [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://mlsanity.com/paper/2104.08773/) (2021)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Do Prompt-Based Models Really Understand the Meaning of their Prompts?](https://mlsanity.com/paper/2109.01247/) (2021)
- [Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning](https://mlsanity.com/paper/2302.02662/) (2023)
- [We're Afraid Language Models Aren't Modeling Ambiguity](https://mlsanity.com/paper/2304.14399/) (2023)
- ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://mlsanity.com/paper/1909.03065/) (2019)
- [Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color](https://mlsanity.com/paper/2109.06129/) (2021)
- [Mind's Eye: Grounded Language Model Reasoning through Simulation](https://mlsanity.com/paper/2210.05359/) (2022)
- [Collaborating with language models for embodied reasoning](https://mlsanity.com/paper/2302.00763/) (2023)
- [Grounding Complex Natural Language Commands for Temporal Tasks in Unseen Environments](https://mlsanity.com/paper/2302.11649/) (2023)
- [Do Large Language Models Know about Facts?](https://mlsanity.com/paper/2310.05177/) (2023)
- [Grounding 'Grounding' in NLP](https://mlsanity.com/paper/2106.02192/) (2021)
- [MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators](https://mlsanity.com/paper/2110.06609/) (2021)
- [Geographic and Geopolitical Biases of Language Models](https://mlsanity.com/paper/2212.10408/) (2022)
- [Does Vision-and-Language Pretraining Improve Lexical Grounding?](https://mlsanity.com/paper/2109.10246/) (2021)
- [A Neural Model for User Geolocation and Lexical Dialectology](https://mlsanity.com/paper/1704.04008/) (2017)
- [Conditional Generation of Temporally-ordered Event Sequences](https://mlsanity.com/paper/2012.15786/) (2020)
- [Pretraining on Interactions for Learning Grounded Affordance Representations](https://mlsanity.com/paper/2207.02272/) (2022)

## Built on by (most cited first, 27 of 27)

- [TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models](https://mlsanity.com/paper/2311.17667/) (2023)
- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue](https://mlsanity.com/paper/2509.10852/) (2025)
- [MuLan: A Study of Fact Mutability in Language Models](https://mlsanity.com/paper/2404.03036/) (2024)
- [LexTime: A Benchmark for Temporal Ordering of Legal Events](https://mlsanity.com/paper/2506.04041/) (2025)
- [TCP: a Benchmark for Temporal Constraint-Based Planning](https://mlsanity.com/paper/2505.19927/) (2025)
- [Attribution, Citation, and Quotation: A Survey of Evidence-based Text Generation with Large Language Models](https://mlsanity.com/paper/2508.15396/) (2025)
- [Counterfactual-Consistency Prompting for Relative Temporal Understanding in Large Language Models](https://mlsanity.com/paper/2502.11425/) (2025)
- [Self-Improving World Modelling with Latent Actions](https://mlsanity.com/paper/2602.06130/) (2026)
- [Remember This Event That Year? Assessing Temporal Information and Reasoning in Large Language Models](https://mlsanity.com/paper/2402.11997/) (2024)
- [Temporal reasoning for timeline summarisation in social media](https://mlsanity.com/paper/2501.00152/) (2024)
- [RAG or Learning? Understanding the Limits of LLM Adaptation under Continuous Knowledge Drift in the Real World](https://mlsanity.com/paper/2604.05096/) (2026)
- [Look It Up: Analysing Internal Web Search Capabilities of Modern LLMs](https://mlsanity.com/paper/2511.18931/) (2025)
- [Formulation Comparison for Timeline Construction using LLMs](https://mlsanity.com/paper/2403.00990/) (2024)
- [RASTeR: Robust, Agentic, and Structured Temporal Reasoning](https://mlsanity.com/paper/2406.19538/) (2024)
- [Detecting LLM Fact-conflicting Hallucinations Enhanced by Temporal-logic-based Reasoning](https://mlsanity.com/paper/2502.13416/) (2025)
- [Can VLMs Predict Future States? Bootstrapping World Models from Inverse Dynamics](https://mlsanity.com/paper/2506.06006/) (2025)
- [Lost in Space? Vision-Language Models Struggle with Relative Camera Pose Estimation](https://mlsanity.com/paper/2601.22228/) (2026)
- [Measuring temporal effects of agent knowledge by date-controlled tool use](https://mlsanity.com/paper/2503.04188/) (2025)
- [Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning](https://mlsanity.com/paper/2509.00975/) (2025)
- [Correct-Detect: Balancing Performance and Ambiguity Through the Lens of Coreference Resolution in LLMs](https://mlsanity.com/paper/2509.14456/) (2025)
- [UT-ACA: Uncertainty-Triggered Adaptive Context Allocation for Long-Context Inference](https://mlsanity.com/paper/2603.18446/) (2026)
- [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)
- [Long-term Measurements: Towards a Longitudinal Understanding of Human-AI Interactions](https://mlsanity.com/paper/2608.02491/) (2026)
- [Evaluating Multi-Dimensional Generalization of Large Language Models in Temporal Extraction Tasks](https://mlsanity.com/paper/2610.02549/) (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)

Are Large language models (LLMs) temporally grounded? Since LLMs cannot perceive and interact with the environment, it is impossible to answer this question directly. Instead, we provide LLMs with textual narratives and probe them with respect to their common-sense knowledge of the structure and duration of events, their ability to order events along a timeline, and self-consistency within their temporal model (e.g., temporal relations such as after and before are mutually exclusive for any pair of events). We evaluate state-of-the-art LLMs (such as LLaMA 2 and GPT-4) on three tasks reflecting these abilities. Generally, we find that LLMs lag significantly behind both human performance as well as small-scale, specialised LMs. In-context learning, instruction tuning, and chain-of-thought prompting reduce this gap only to a limited degree. Crucially, LLMs struggle the most with self-consistency, displaying incoherent behaviour in at least 27.23% of their predictions. Contrary to expectations, we also find that scaling the model size does not guarantee positive gains in performance. To explain these results, we study the sources from which LLMs may gather temporal information: we find that sentence ordering in unlabelled texts, available during pre-training, is only weakly correlated with event ordering. Moreover, public instruction tuning mixtures contain few temporal tasks. Hence, we conclude that current LLMs lack a consistent temporal model of textual narratives. Code, datasets, and LLM outputs are available at https://github.com/yfqiu-nlp/temporal-llms.