Large Language Models (LLMs) are getting smarter, but how well do they truly understand the nuances of different cultures? A new benchmark called CulturalBench is putting LLMs to the test, revealing some surprising gaps in their cultural knowledge. CulturalBench consists of over 1,200 carefully crafted questions covering 45 global regions and diverse topics like food preferences, greeting etiquette, and social customs. Unlike previous benchmarks that often rely on easily scraped web data, CulturalBench questions are human-written and verified by multiple annotators, making it a more robust and challenging assessment. The benchmark uses two testing formats: an easier multiple-choice version and a harder true/false version. Interestingly, LLMs performed much worse on the true/false version, suggesting they might rely on simple heuristics in multiple-choice formats. The results show that even the most advanced LLMs struggle with questions that have multiple correct answers, revealing a tendency to stick to a single answer, likely reflecting cultural stereotypes. While models performed reasonably well on questions related to North America, South Asia, and Western/Southern Europe, they consistently underperformed on regions like South America, Eastern Europe, and the Middle East. This highlights the uneven representation of cultural knowledge in training data. The research also points out that simply being proficient in multiple languages doesn't equate to cultural understanding. For example, Chinese and European models weren’t necessarily stronger on questions related to their own regions. CulturalBench is a significant step toward developing more culturally sensitive AI. It’s not just about making LLMs more accurate but also about ensuring they’re inclusive and respectful of the diversity of human experience. As researchers continue to refine these benchmarks, the hope is that LLM developers will use them to improve their models, leading to AI that truly understands and respects the richness of global cultures.