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This research introduces multilingual story moral generation as a novel benchmark to assess how well frontier LLMs capture cultural diversity versus converging on narrow, universal values. While models like GPT-4o and Gemini can approximate central human moral interpretations, they struggle to replicate the cross-linguistic variation inherent in human narrative understanding. This work shifts the focus of cultural alignment evaluation beyond static knowledge tests toward dynamic, culturally grounded tasks that measure a model's ability to reflect diverse societal values.
Lessons Without Borders? Evaluating Cultural Alignment of LLMs Using Multilingual Story Moral Generation
arXiv:2604.08797v2 Announce Type: replace-cross Abstract: Stories are key to transmitting values across cultures, but their interpretation varies across linguistic and cultural contexts. Thus, we introduce multilingual story moral generation as a novel culturally grounded evaluation task. Using a new dataset of human-written story morals collected across 14 language-culture pairs, we compare model outputs with human interpretations via semantic similarity, a human preference survey, and value categorization. We show that frontier models such as GPT-4o and Gemini generate story morals that are semantically similar to human responses and preferred by human evaluators. However, their outputs exhibit markedly less cross-linguistic variation and concentrate on a narrower set of widely shared values. These findings suggest that while contemporary models can approximate central tendencies of human moral interpretation, they struggle to reproduce the diversity that characterizes human narrative understanding. By framing narrative interpretation as an evaluative task, this work introduces a new approach to studying cultural alignment in language models beyond static benchmarks or knowledge-based tests.
Title:Lessons Without Borders? Evaluating Cultural Alignment of LLMs Using Multilingual Story Moral Generation
Abstract:Stories are key to transmitting values across cultures, but their interpretation varies across linguistic and cultural contexts. Thus, we introduce multilingual story moral generation as a novel culturally grounded evaluation task. Using a new dataset of human-written story morals collected across 14 language-culture pairs, we compare model outputs with human interpretations via semantic similarity, a human preference survey, and value categorization. We show that frontier models such as GPT-4o and Gemini generate story morals that are semantically similar to human responses and preferred by human evaluators. However, their outputs exhibit markedly less cross-linguistic variation and concentrate on a narrower set of widely shared values. These findings suggest that while contemporary models can approximate central tendencies of human moral interpretation, they struggle to reproduce the diversity that characterizes human narrative understanding. By framing narrative interpretation as an evaluative task, this work introduces a new approach to studying cultural alignment in language models beyond static benchmarks or knowledge-based tests.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2604.08797 [cs.CL]
(or arXiv:2604.08797v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2604.08797 Focus to learn more arXiv-issued DOI via DataCite
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