This paper investigates the capabilities and limitations of large language models (LLMs) in understanding cultural commonsense. The study evaluates several state-of-the-art LLMs on tasks involving cultural-specific and general commonsense knowledge. The results show that LLMs exhibit significant performance discrepancies when tested on culture-specific knowledge, with some cultures being less represented in their training data. Additionally, LLMs tend to associate general commonsense knowledge with a few dominant cultures, and their performance is affected by the language used in queries. The study also highlights that the language used to prompt LLMs can significantly impact their ability to understand cultural commonsense. The findings suggest that LLMs are biased towards cultures that are well-represented in their training data, and that their performance on cultural commonsense tasks is uneven. The paper provides insights into how to improve the cultural awareness of LLMs, including the need for more diverse training data and better instruction-tuning strategies. The study also emphasizes the importance of considering cultural context when evaluating LLMs, as the language used in queries can influence their performance. Overall, the research highlights the need for further investigation into the cultural biases of LLMs and the development of more culturally-aware models.This paper investigates the capabilities and limitations of large language models (LLMs) in understanding cultural commonsense. The study evaluates several state-of-the-art LLMs on tasks involving cultural-specific and general commonsense knowledge. The results show that LLMs exhibit significant performance discrepancies when tested on culture-specific knowledge, with some cultures being less represented in their training data. Additionally, LLMs tend to associate general commonsense knowledge with a few dominant cultures, and their performance is affected by the language used in queries. The study also highlights that the language used to prompt LLMs can significantly impact their ability to understand cultural commonsense. The findings suggest that LLMs are biased towards cultures that are well-represented in their training data, and that their performance on cultural commonsense tasks is uneven. The paper provides insights into how to improve the cultural awareness of LLMs, including the need for more diverse training data and better instruction-tuning strategies. The study also emphasizes the importance of considering cultural context when evaluating LLMs, as the language used in queries can influence their performance. Overall, the research highlights the need for further investigation into the cultural biases of LLMs and the development of more culturally-aware models.