Exploring Cultural Variations in Moral Judgments with Large Language Models

TL;DR

This study evaluates how well large language models reflect diverse moral values across cultures, finding that instruction tuning and scale improve alignment with human moral judgments, especially in Western regions.

Contribution

It systematically compares various LLMs against global survey data to assess their ability to mirror culturally diverse moral attitudes, highlighting the impact of instruction tuning and size.

Findings

Advanced instruction-tuned models show higher correlation with human moral judgments.

Models align better with Western and educated regions than others.

Scaling and instruction tuning improve cultural sensitivity of LLMs.

Abstract

Large Language Models (LLMs) have shown strong performance across many tasks, but their ability to capture culturally diverse moral values remains unclear. In this paper, we examine whether LLMs mirror variations in moral attitudes reported by the World Values Survey (WVS) and the Pew Research Center's Global Attitudes Survey (PEW). We compare smaller monolingual and multilingual models (GPT-2, OPT, BLOOMZ, and Qwen) with recent instruction-tuned models (GPT-4o, GPT-4o-mini, Gemma-2-9b-it, and Llama-3.3-70B-Instruct). Using log-probability-based \emph{moral justifiability} scores, we correlate each model's outputs with survey data covering a broad set of ethical topics. Our results show that many earlier or smaller models often produce near-zero or negative correlations with human judgments. In contrast, advanced instruction-tuned models achieve substantially higher positive correlations, suggesting they better reflect real-world moral attitudes. We provide a detailed regional analysis revealing that models align better with Western, Educated, Industrialized, Rich, and Democratic (W.E.I.R.D.) nations than with other regions. While scaling model size and using instruction tuning improves alignment with cross-cultural moral norms, challenges remain for certain topics and regions. We discuss these findings in relation to bias analysis, training data diversity, information retrieval implications, and strategies for improving the cultural sensitivity of LLMs.