Abstract
As Large Language Models (LLMs) are increasingly deployed in global applications, understanding how they represent and reason about moral values across different cultures becomes critical. This research investigates whether LLMs can accurately capture cultural variations in moral reasoning using data from the World Values Survey (WVS).
We introduce log-probability-based moral justifiability scores to assess how well different LLMs align with culturally diverse moral perspectives. Our analysis compares smaller models (GPT-2, OPT, BLOOMZ, Qwen) with advanced instruction-tuned models (GPT-4o, Gemma-2-9b-it, Llama-3.3-70B-Instruct), finding that instruction-tuned models achieved substantially higher positive correlations with human moral judgments across cultures.
This work has important implications for the responsible deployment of AI systems in culturally diverse contexts and highlights the need for evaluation methodologies that account for cultural variations in moral reasoning.
Theoretical Framework
Hofstede’s Cultural Dimensions
- Power Distance
- Individualism vs. Collectivism
- Uncertainty Avoidance
- Masculinity vs. Femininity
- Long-Term vs. Short-Term Orientation
Moral Foundations Theory
- Care/Harm
- Fairness/Cheating
- Loyalty/Betrayal
- Authority/Subversion
- Sanctity/Degradation
Methodology
Survey Items as Ground Truth
Moral-justifiability items drawn from the World Values Survey and the Pew Research Center Global Attitudes Survey, so the reference is existing cross-national survey data rather than newly written scenarios.
Model Scoring
Eight decoder-only models scored the same items: GPT-2, OPT, BLOOMZ and Qwen among the smaller models, and GPT-4o, GPT-4o-mini, Gemma-2-9b-it and Llama-3.3-70B-Instruct among the instruction-tuned ones.
Log-Probability Elicitation
Each model's judgement is read from the log-probabilities it assigns to the survey response options, so no human annotation step is involved.
Cross-Cultural Comparison
Model scores are correlated against each country's survey responses, which is what exposes the gap between W.E.I.R.D. and non-W.E.I.R.D. nations.
Key Findings
Implications
- Risk of Western moral framework dominance when deploying LLMs in global AI systems and applications.
- Need for culturally-diverse training data and evaluation metrics that go beyond English-centric benchmarks.
- Importance of including diverse moral frameworks in AI alignment research and safety considerations.
- Cautious deployment recommended for culturally-sensitive applications such as education and healthcare.
Citation
@article{mohammadi2024cultural,
title={Exploring Cultural Variations in Moral Judgments with Large Language Models},
author={Mohammadi, Hadi and Bagheri, Robert A.},
journal={Computational Linguistics in the Netherlands Journal},
volume={15},
pages={59--77},
year={2026}
}