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

TL;DR

This paper analyzes how large language models reflect cultural values, revealing that models trained on Chinese data prioritize collectivist values, highlighting cultural biases and the need for diverse AI alignment strategies.

Contribution

It introduces a prompt-based method to assess cultural value alignment in LLMs and demonstrates how training data influences model value preferences, emphasizing cultural biases.

Findings

DeepSeek downplays self-enhancement values compared to Western models.

All models highly prioritize self-transcendence values.

Cultural biases in LLMs reflect training data origins.

Abstract

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.