# ChronoCulture: Evaluating Cultural Awareness Across Time in Large Language Models

arXiv: https://arxiv.org/abs/2610.20472 · HTML: https://mlsanity.com/paper/2610.20472/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 42 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Builds on (most cited first, 27 of 27)

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- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://mlsanity.com/paper/2412.03304/) (2024)
- [BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages](https://mlsanity.com/paper/2406.09948/) (2024)
- [A Dataset for Answering Time-Sensitive Questions](https://mlsanity.com/paper/2108.06314/) (2021)
- [INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge](https://mlsanity.com/paper/2411.19799/) (2024)
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- [WorldValuesBench: A Large-Scale Benchmark Dataset for Multi-Cultural Value Awareness of Language Models](https://mlsanity.com/paper/2404.16308/) (2024)
- [NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models](https://mlsanity.com/paper/2404.12464/) (2024)
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- [IndoCulture: Exploring Geographically-Influenced Cultural Commonsense Reasoning Across Eleven Indonesian Provinces](https://mlsanity.com/paper/2404.01854/) (2024)
- [PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture](https://mlsanity.com/paper/2509.02550/) (2025)
- [GIMMICK -- Globally Inclusive Multimodal Multitask Cultural Knowledge Benchmarking](https://mlsanity.com/paper/2502.13766/) (2025)
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- [On the Alignment of Large Language Models with Global Human Opinion](https://mlsanity.com/paper/2509.01418/) (2025)
- [Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts](https://mlsanity.com/paper/2502.14865/) (2025)
- [RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture Understanding](https://mlsanity.com/paper/2505.14462/) (2025)
- [IndicVisionBench: Benchmarking Cultural and Multilingual Understanding in VLMs](https://mlsanity.com/paper/2511.04727/) (2025)
- [When Benchmarks Age: Temporal Misalignment through Large Language Model Factuality Evaluation](https://mlsanity.com/paper/2510.07238/) (2025)
- [Characterizing the ability of LLMs to recapitulate Americans' distributional responses to public opinion polling questions across political issues](https://mlsanity.com/paper/2603.20229/) (2026)
- [Accurate in space, unreliable in time: how LLMs represent national cultural change](https://mlsanity.com/paper/2609.01902/) (2026)

## Abstract (arXiv)

Cultural attitudes and social norms evolve over time, yet most cultural benchmarks for large language models (LLMs) treat culture as a static attribute of a country or region. We introduce ChronoCulture, a benchmark of 10,360 temporal pairs built from longitudinal survey data across 72 countries. Each pair contains the same survey question asked in the same country in an older and a newer year, with full population response distributions as ground truth. Across six LLMs, we find that models can often predict a country's attitudes at one point in time but do not reliably capture how those attitudes change. Their predictions for different years tend to stay close together, failing to capture much of the observed change. Specifying the intended year in the prompt also offers little overall improvement in alignment with survey responses. These findings suggest that models often present a largely static picture of cultural attitudes, highlighting the need to evaluate cultural awareness across time.