# Whose Morality Do They Speak? Unraveling Cultural Bias in Multilingual Language Models

arXiv: https://arxiv.org/abs/2412.18863 · HTML: https://mlsanity.com/paper/2412.18863/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 25 of its 63 references that are in the map.
- First posted on arXiv 25 Dec 2024.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 25 of 25)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [Social Chemistry 101: Learning to Reason about Social and Moral Norms](https://mlsanity.com/paper/2011.00620/) (2020)
- [Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting](https://mlsanity.com/paper/2305.07004/) (2023)
- [Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis](https://mlsanity.com/paper/2304.04675/) (2023)
- [Having Beer after Prayer? Measuring Cultural Bias in Large Language Models](https://mlsanity.com/paper/2305.14456/) (2023)
- [Probing Pre-Trained Language Models for Cross-Cultural Differences in Values](https://mlsanity.com/paper/2203.13722/) (2022)
- [Can Machines Learn Morality? The Delphi Experiment](https://mlsanity.com/paper/2110.07574/) (2021)
- [Moral Foundations of Large Language Models](https://mlsanity.com/paper/2310.15337/) (2023)
- [MoralBench: Moral Evaluation of LLMs](https://mlsanity.com/paper/2406.04428/) (2024)
- [Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting](https://mlsanity.com/paper/2310.16523/) (2023)
- [Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts](https://mlsanity.com/paper/2306.11372/) (2023)
- [Speaking Multiple Languages Affects the Moral Bias of Language Models](https://mlsanity.com/paper/2211.07733/) (2022)
- [Assessing LLMs for Moral Value Pluralism](https://mlsanity.com/paper/2312.10075/) (2023)
- [Does Moral Code Have a Moral Code? Probing Delphi's Moral Philosophy](https://mlsanity.com/paper/2205.12771/) (2022)
- [Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ](https://mlsanity.com/paper/2403.03814/) (2024)
- [Exploring and steering the moral compass of Large Language Models](https://mlsanity.com/paper/2405.17345/) (2024)
- [Decoding Multilingual Moral Preferences: Unveiling LLM's Biases Through the Moral Machine Experiment](https://mlsanity.com/paper/2407.15184/) (2024)
- [SaGE: Evaluating Moral Consistency in Large Language Models](https://mlsanity.com/paper/2402.13709/) (2024)

## Built on by (most cited first, 17 of 17)

- [Tracing Moral Foundations in Large Language Models](https://mlsanity.com/paper/2601.05437/) (2026)
- [Made-in China, Thinking in America:U.S. Values Persist in Chinese LLMs](https://mlsanity.com/paper/2512.13723/) (2025)
- [One Model, Many Morals: Uncovering Cross-Linguistic Misalignments in Computational Moral Reasoning](https://mlsanity.com/paper/2509.21443/) (2025)
- [Moral Susceptibility and Robustness under Persona Role-Play in Large Language Models](https://mlsanity.com/paper/2511.08565/) (2025)
- [On the Credibility of Evaluating LLMs using Survey Questions](https://mlsanity.com/paper/2602.04033/) (2026)
- [Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI](https://mlsanity.com/paper/2505.14435/) (2025)
- [Evaluating Modern Large Language Models on Low-Resource and Morphologically Rich Languages:A Cross-Lingual Benchmark Across Cantonese, Japanese, and Turkish](https://mlsanity.com/paper/2511.10664/) (2025)
- [What if I ask in textitalia lingua? Measuring Functional Similarity Across Languages](https://mlsanity.com/paper/2509.04032/) (2025)
- [Language Models Entangle Language and Culture](https://mlsanity.com/paper/2601.15337/) (2026)
- [Investigating the Influence of Language on Sycophantic Behavior of Multilingual LLMs](https://mlsanity.com/paper/2603.27664/) (2026)
- [Moral Semantics Survive Machine Translation: Cross-Lingual Evidence from Moral Foundations Corpora](https://mlsanity.com/paper/2605.22660/) (2026)
- [ALIGN: Word Association Learning for Cultural Alignment in Large Language Models](https://mlsanity.com/paper/2508.13426/) (2025)
- [Demographic Biases and Gaps in the Perception of Sexism in Large Language Models](https://mlsanity.com/paper/2508.18245/) (2025)
- [The Curious Case of Curiosity across Human Cultures and LLMs](https://mlsanity.com/paper/2510.12943/) (2025)
- [A Detailed Study on LLM Biases Concerning Corporate Social Responsibility and Green Supply Chains](https://mlsanity.com/paper/2511.01840/) (2025)
- [Whose Norms? Disentangling Cultural and Personal Alignment in Large Language Models](https://mlsanity.com/paper/2606.07877/) (2026)
- [Steering LLMs Responses Towards Moral Foundations on the Norwegian MFQ-30](https://mlsanity.com/paper/2609.21636/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have become integral tools in diverse domains, yet their moral reasoning capabilities across cultural and linguistic contexts remain underexplored. This study investigates whether multilingual LLMs, such as GPT-3.5-Turbo, GPT-4o-mini, Llama 3.1, and MistralNeMo, reflect culturally specific moral values or impose dominant moral norms, particularly those rooted in English. Using the updated Moral Foundations Questionnaire (MFQ-2) in eight languages, Arabic, Farsi, English, Spanish, Japanese, Chinese, French, and Russian, the study analyzes the models' adherence to six core moral foundations: care, equality, proportionality, loyalty, authority, and purity. The results reveal significant cultural and linguistic variability, challenging the assumption of universal moral consistency in LLMs. Although some models demonstrate adaptability to diverse contexts, others exhibit biases influenced by the composition of the training data. These findings underscore the need for culturally inclusive model development to improve fairness and trust in multilingual AI systems.