Do Large Language Models Understand Morality Across Cultures? (2025)

Abstract: Recent advancements in large language models (LLMs) have established them as powerful tools across numerous domains. However, persistent concerns about embedded biases, such as gender, racial, and cultural biases arising from their training data, raise significant questions about the ethical use and societal consequences of these technologies. This study investigates the extent to which LLMs capture cross-cultural differences and similarities in moral perspectives. Specifically, we examine whether LLM outputs align with patterns observed in international survey data on moral attitudes. To this end, we employ three complementary methods: (1) comparing variances in moral scores produced by models versus those reported in surveys, (2) conducting cluster alignment analyses to assess correspondence between country groupings derived from LLM outputs and survey data, and (3) directly probing models with comparative prompts using systematically chosen token pairs. Our results reveal that current LLMs often fail to reproduce the full spectrum of cross-cultural moral variation, tending to compress differences and exhibit low alignment with empirical survey patterns. These findings highlight a pressing need for more robust approaches to mitigate biases and improve cultural representativeness in LLMs. We conclude by discussing the implications for the responsible development and global deployment of LLMs, emphasizing fairness and ethical alignment.

## introduction large language models (llms) have recently taken center stage in both scientific and public debates due to significant advancements in their performance @xcite . these models now show great promise for applications ranging from search engines and recommendation systems to automated decision-making tools that deeply influence everyday life. nonetheless, alongside these impressive capabilities, concerns persist regarding the potential biases llms can exhibit, such as gender, racial, and cultural bias. a primary reason for this risk is that llms learn from vast, realworld text datasets that may contain societal and cultural prejudices @xcite . consequently, when large portions of the training data systematically reflect certain groups unfavorably, the resulting language model may replicate or even amplify those biases. given the growing reliance on llm-based systems across many fields, this raises important questions about whether these models truly capture the diverse moral perspectives observed in actual human societies. despite its importance, the issue of whether llms accurately reflect cross-cultural moral judgments has been relatively understudied @xcite . in examining how faithfully llms capture moral attitudes that vary across cultural contexts, a key consideration is their ability to replicate both the areas of divergence (where cultures disagree) and similarity (where cultures align) on moral topics. thus, the central research question is: to what extent do language models capture cultural diversity and common tendencies regarding topics on which people around the world tend to diverge or agree in their moral judgments? addressing this question carries both scientific and societal significance. scientifically, it provides insight into how effectively llms, trained primarily on text data, can model complex cultural norms. societally, ensuring these models reflect actual cross-cultural variation is vital for preventing biased or inaccurate representations of different cultural groups @xcite . as llms increasingly shape public opinion and decision-making, a mismatch between how cultures truly view moral issues and how models characterize these issues can perpetuate prejudice and unfairness. conversely, llms that accurately capture inter-cultural moral differences and similarities can help reveal common ground and support cross-cultural understanding. against this backdrop, the present study focuses on evaluating the extent to which contemporary llms mirror the diversity and patterns of moral judgments observed across cultures. three primary methods are employed: by using these complementary techniques, this work offers insights into the strengths and limitations of llms in depicting cross-cultural moral norms, ultimately informing ongoing discussions about their ethical development and deployment. the remainder of this paper is structured as follows. in section 2, we review related research on cross-cultural moral judgments in llms and the issue of bias in these models. section 3 describes the data and methods used in our analysis, and section 4 details the results. we then discuss key findings in and conclude with final remarks in section 5. ## results to evaluate how well language models capture cross-cultural moral variability, we compare the topic-level variance from two survey datasets (wvs and pew) with the variance of the corresponding model-generated moral scores. ## discussion and conclusion the findings of this study shed light on the capability of llms to accurately capture cultural diversity and common tendencies across different moral topics. the investigation utilized multiple methodologies that were based on probing llms with prompts derived from the world values survey (wvs) and pew datasets, focusing on a range of moral topics.