The Cultural Gene of Large Language Models: A Study on the Impact of Cross-Corpus Training on Model Values and Biases

2025-08-17

AI Summary

Large language models (LLMs) implicitly inherit cultural values from their training corpora, yet systematic, quantifiable assessment of such value alignment remains lacking. Method: We introduce the concept of “cultural memes”—systematically internalized value orientations—and focus on two cross-cultural dimensions: Individualism–Collectivism (IDV) and Power Distance (PDI). We construct the Cultural Probe Dataset (CPD) and propose the Cultural Alignment Index (CAI), evaluating LLMs via zero-shot prompting, human annotation, statistical testing, and correlation with Hofstede’s national culture scores. Contribution/Results: Experiments reveal GPT-4 exhibits strong individualist and low-power-distance preferences, whereas ERNIE Bot favors collectivism and high power distance; both achieve CAI > 0.8 (p < 0.001), confirming LLMs statistically mirror the cultural profiles of their training data. This work establishes the first measurable, comparable, and interpretable framework for assessing cultural value alignment in LLMs.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large language models (LLMs) are deployed globally, yet their underlying cultural and ethical assumptions remain underexplored. We propose the notion of a "cultural gene" -- a systematic value orientation that LLMs inherit from their training corpora -- and introduce a Cultural Probe Dataset (CPD) of 200 prompts targeting two classic cross-cultural dimensions: Individualism-Collectivism (IDV) and Power Distance (PDI). Using standardized zero-shot prompts, we compare a Western-centric model (GPT-4) and an Eastern-centric model (ERNIE Bot). Human annotation shows significant and consistent divergence across both dimensions. GPT-4 exhibits individualistic and low-power-distance tendencies (IDV score approx 1.21; PDI score approx -1.05), while ERNIE Bot shows collectivistic and higher-power-distance tendencies (IDV approx -0.89; PDI approx 0.76); differences are statistically significant (p < 0.001). We further compute a Cultural Alignment Index (CAI) against Hofstede's national scores and find GPT-4 aligns more closely with the USA (e.g., IDV CAI approx 0.91; PDI CAI approx 0.88) whereas ERNIE Bot aligns more closely with China (IDV CAI approx 0.85; PDI CAI approx 0.81). Qualitative analyses of dilemma resolution and authority-related judgments illustrate how these orientations surface in reasoning. Our results support the view that LLMs function as statistical mirrors of their cultural corpora and motivate culturally aware evaluation and deployment to avoid algorithmic cultural hegemony.

Problem

• Examining cultural biases in large language models from training data
• Comparing Western and Eastern models on individualism and power distance
• Proposing culturally aware evaluation to prevent algorithmic cultural hegemony

Innovation

• Cultural Probe Dataset for value assessment
• Cultural Alignment Index comparison method
• Standardized zero-shot prompts for analysis