New research reveals that multilingual NLP models lack cultural competence despite language coverage, requiring rethinking of benchmark design and data practices.

A comprehensive synthesis of 50+ papers (2020-2026) shows multilingual capability doesn't guarantee cultural understanding in NLP systems

Training data coverage alone is insufficient; tokenization, prompt language, and culturally specific supervision significantly impact model performance

New culture-aware benchmarks including Global-MMLU, CDEval, WorldValuesBench, and CulturalBench are emerging to better evaluate cultural alignment

Multimodal context and community-grounded data practices are critical factors that existing benchmark designs often overlook

Current translated benchmarks and global evaluation metrics fail to capture culturally specific requirements and local knowledge