# LLMs and Cultural Values: the Impact of Prompt Language and Explicit Cultural Framing

arXiv: https://arxiv.org/abs/2511.03980 · HTML: https://mlsanity.com/paper/2511.03980/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2025.

## Built on by (most cited first, 17 of 17)

- [On the Credibility of Evaluating LLMs using Survey Questions](https://mlsanity.com/paper/2602.04033/) (2026)
- [Multilingual Agent-Based World Modeling for Social Science](https://mlsanity.com/paper/2512.07195/) (2025)
- [Language Shapes Mental Health Evaluations in Large Language Models](https://mlsanity.com/paper/2603.06910/) (2026)
- [Tastes without distinction: silicon samples and the synthetic construction of tastes](https://mlsanity.com/paper/2606.30085/) (2026)
- [Prompt Programming for Cultural Bias and Alignment of Large Language Models](https://mlsanity.com/paper/2603.16827/) (2026)
- [Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook](https://mlsanity.com/paper/2604.06210/) (2026)
- [Why are all LLMs Obsessed with Japanese Culture? On the Hidden Cultural and Regional Biases of LLMs](https://mlsanity.com/paper/2604.21751/) (2026)
- [Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisation](https://mlsanity.com/paper/2605.12515/) (2026)
- [Improving Cross-Cultural Survey Simulation with Calibrated Value Personas](https://mlsanity.com/paper/2605.16193/) (2026)
- [Accurate in space, unreliable in time: how LLMs represent national cultural change](https://mlsanity.com/paper/2609.01902/) (2026)
- [Calibrated to Whom? Persona and Language Effects on Cultural Values in JEV](https://mlsanity.com/paper/2609.36399/) (2026)
- [Disentangling Prompt Element Level Risk Factors for Hallucinations and Omissions in Mental Health LLM Responses](https://mlsanity.com/paper/2604.00014/) (2026)
- [Which Institutional Frameworks Do Chatbots Assume? Auditing Jurisdictional Defaults in Multilingual LLMs](https://mlsanity.com/paper/2606.00333/) (2026)
- [Hybrid Panels: Toward Human-AI Collaboration in Survey Research](https://mlsanity.com/paper/2608.22582/) (2026)
- [WildSEEK: Evaluating Language Models for Information-Seeking](https://mlsanity.com/paper/2608.30683/) (2026)
- [Personas Differ from Native-Language Generation: Language Pathways Shape LLM Interpersonal Advice](https://mlsanity.com/paper/2608.30873/) (2026)
- [Beyond Cultural Knowledge: Evaluating Arabic Cultural Appropriateness of Large Language Models](https://mlsanity.com/paper/2609.16006/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are rapidly being adopted by users across the globe, who interact with them in a diverse range of languages. At the same time, there are well-documented imbalances in the training data and optimisation objectives of this technology, raising doubts as to whether LLMs can represent the cultural diversity of their broad user base. In this study, we look at LLMs and cultural values and examine how prompt language and cultural framing influence model responses and their alignment with human values in different countries. We probe 10 LLMs with 63 items from the Hofstede Values Survey Module and World Values Survey, translated into 11 languages, and formulated as prompts with and without different explicit cultural perspectives. Our study confirms that both prompt language and cultural perspective produce variation in LLM outputs, but with an important caveat: While targeted prompting can, to a certain extent, steer LLM responses in the direction of the predominant values of the corresponding countries, it does not overcome the models' systematic bias toward the values associated with a restricted set of countries in our dataset: the Netherlands, Germany, the US, and Japan. All tested models, regardless of their origin, exhibit remarkably similar patterns: They produce fairly neutral responses on most topics, with selective progressive stances on issues such as social tolerance. Alignment with cultural values of human respondents is improved more with an explicit cultural perspective than with a targeted prompt language. Unexpectedly, combining both approaches is no more effective than cultural framing with an English prompt. These findings reveal that LLMs occupy an uncomfortable middle ground: They are responsive enough to changes in prompts to produce variation, but too firmly anchored to specific cultural defaults to adequately represent cultural diversity.