# Cultural Value Alignment in Large Language Models: A Prompt-based Analysis of Schwartz Values in Gemini, ChatGPT, and DeepSeek

arXiv: https://arxiv.org/abs/2505.17112 · HTML: https://mlsanity.com/paper/2505.17112/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 2 of 2)

- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Strong and weak alignment of large language models with human values](https://mlsanity.com/paper/2408.04655/) (2024)

## Built on by (most cited first, 4 of 4)

- [Do Schwartz Higher-Order Values Help Sentence-Level Human Value Detection? A Study of Hierarchical Gating and Calibration](https://mlsanity.com/paper/2602.00913/) (2026)
- [Value Alignment Tax: Measuring Value Trade-offs in LLM Alignment](https://mlsanity.com/paper/2602.12134/) (2026)
- [KVpop -- Key-Value Cache Compression with Predictive Online Pruning](https://mlsanity.com/paper/2607.05061/) (2026)
- [The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment](https://mlsanity.com/paper/2607.05552/) (2026)

## Abstract (arXiv)

This study examines cultural value alignment in large language models (LLMs) by analyzing how Gemini, ChatGPT, and DeepSeek prioritize values from Schwartz's value framework. Using the 40-item Portrait Values Questionnaire, we assessed whether DeepSeek, trained on Chinese-language data, exhibits distinct value preferences compared to Western models. Results of a Bayesian ordinal regression model show that self-transcendence values (e.g., benevolence, universalism) were highly prioritized across all models, reflecting a general LLM tendency to emphasize prosocial values. However, DeepSeek uniquely downplayed self-enhancement values (e.g., power, achievement) compared to ChatGPT and Gemini, aligning with collectivist cultural tendencies. These findings suggest that LLMs reflect culturally situated biases rather than a universal ethical framework. To address value asymmetries in LLMs, we propose multi-perspective reasoning, self-reflective feedback, and dynamic contextualization. This study contributes to discussions on AI fairness, cultural neutrality, and the need for pluralistic AI alignment frameworks that integrate diverse moral perspectives.