Researchers introduced multilingual story moral generation as a novel culturally grounded evaluation task to assess the cultural alignment of large language models (LLMs). They compared model outputs with human interpretations, finding that while LLMs can approximate central tendencies of human moral interpretation, they struggle to reproduce cross-linguistic variation and diverse values. This study suggests a new approach to studying cultural alignment in LLMs beyond static benchmarks or knowledge-based tests.