Title: One Model, Many Morals: Uncovering Cross-Linguistic Misalignments in Computational Moral Reasoning
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Multilingual Moral Reasoning, Culture, Morality, Cross linguistic misalignment

Abstract: Large Language Models (LLMs) are increasingly deployed in multilingual and multicultural environments where moral reasoning is essential for generating ethically appropriate responses. Yet, the dominant pretraining of LLMs on English-language data raises critical concerns about their ability to generalize judgments across diverse linguistic and cultural contexts. In this work, we systematically investigate how language mediates moral decision-making in LLMs. We translate two established moral reasoning benchmarks into five culturally and typologically diverse languages, enabling multilingual zero-shot evaluation. Our analysis reveals significant inconsistencies in LLMs' moral judgments across languages, often reflecting cultural misalignment. Through a combination of carefully constructed research questions, we uncover the underlying drivers of these disparities, ranging from disagreements to reasoning strategies employed by LLMs. Finally, through a case study, we link the role of pretraining data in shaping an LLM's moral compass. Through this work, we distill our insights into a structured typology of moral reasoning errors that calls for more culturally aware AI.

TL;DR: LLMs show inconsistent moral judgments across languages due to English-centric training, revealing cultural misalignments and the need for more culturally aware AI.