This paper introduces a novel approach for massively multicultural knowledge acquisition, aiming to address cultural bias and lack of cultural commonsense knowledge in large language models (LLMs). The authors propose the CultureAtlas dataset, which covers a wide range of sub-country-level geographical regions and ethnolinguistic groups, with data cleaning and preprocessing to ensure textual assertion sentence self-containment and fine-grained cultural profile information extraction. The dataset is designed to facilitate the evaluation of language model performance in culturally diverse contexts and serves as a foundational tool for the development of culturally sensitive and aware language models. The dataset is constructed by collecting positive and negative samples of cultural knowledge assertions from Wikipedia documents, which are then expanded through linked topic pages. Positive samples are sentences that a pretrained LM categorizes as generalizable social or cultural norms, while negative samples are non-factual cultural knowledge assertions cross-validated through web search. The authors also perform information extraction on these samples to derive fine-grained cultural profile fields, including sub-country geographical regions, ethno-linguistic identity, demographics, etc. The dataset includes over 1,089 state or province-level regions, 10,436 city-level regions, and 2,557 ethnolinguistic groups, significantly exceeding prior work in the multi-cultural NLP domain. The dataset comprises high-quality positive and negative data samples with a 90%+ pass rate in data quality check via human assessment. The authors evaluate the performance of state-of-the-art foundation language models on CultureAtlas, demonstrating this new dataset as a useful resource for identifying rooms for improvement in LM cultural awareness and debiasing. The authors also investigate the performance of LLMs in culture reasoning patterns across resource availability and topic domains, finding a general positive correlation between model performance in cultural-aware inference and model parameter size. They also find that LLMs tend to perform better in "education" and "holiday" practices over "clothing" and "cuisine" practices. The authors further analyze the limitations of existing LLMs in understanding nuanced cultural nuances within different situational contexts, finding that lack of fine-grained cultural commonsense knowledge is an area where there remains interesting rooms for improvement. The authors also discuss the importance of cultural knowledge in NLP tasks, highlighting the need for a new framework capable of acquiring cultural knowledge to address cultural imbalances present in existing datasets used for training language models. They emphasize the need for a culturally inclusive approach to language model development, focusing on cultural knowledge acquisition based on fine-grained semantic variations. The authors also discuss ethical considerations and broader impact of their work, emphasizing the importance of balanced representations across cultural groups and the need to address issues of fairness and equity in language model development.This paper introduces a novel approach for massively multicultural knowledge acquisition, aiming to address cultural bias and lack of cultural commonsense knowledge in large language models (LLMs). The authors propose the CultureAtlas dataset, which covers a wide range of sub-country-level geographical regions and ethnolinguistic groups, with data cleaning and preprocessing to ensure textual assertion sentence self-containment and fine-grained cultural profile information extraction. The dataset is designed to facilitate the evaluation of language model performance in culturally diverse contexts and serves as a foundational tool for the development of culturally sensitive and aware language models. The dataset is constructed by collecting positive and negative samples of cultural knowledge assertions from Wikipedia documents, which are then expanded through linked topic pages. Positive samples are sentences that a pretrained LM categorizes as generalizable social or cultural norms, while negative samples are non-factual cultural knowledge assertions cross-validated through web search. The authors also perform information extraction on these samples to derive fine-grained cultural profile fields, including sub-country geographical regions, ethno-linguistic identity, demographics, etc. The dataset includes over 1,089 state or province-level regions, 10,436 city-level regions, and 2,557 ethnolinguistic groups, significantly exceeding prior work in the multi-cultural NLP domain. The dataset comprises high-quality positive and negative data samples with a 90%+ pass rate in data quality check via human assessment. The authors evaluate the performance of state-of-the-art foundation language models on CultureAtlas, demonstrating this new dataset as a useful resource for identifying rooms for improvement in LM cultural awareness and debiasing. The authors also investigate the performance of LLMs in culture reasoning patterns across resource availability and topic domains, finding a general positive correlation between model performance in cultural-aware inference and model parameter size. They also find that LLMs tend to perform better in "education" and "holiday" practices over "clothing" and "cuisine" practices. The authors further analyze the limitations of existing LLMs in understanding nuanced cultural nuances within different situational contexts, finding that lack of fine-grained cultural commonsense knowledge is an area where there remains interesting rooms for improvement. The authors also discuss the importance of cultural knowledge in NLP tasks, highlighting the need for a new framework capable of acquiring cultural knowledge to address cultural imbalances present in existing datasets used for training language models. They emphasize the need for a culturally inclusive approach to language model development, focusing on cultural knowledge acquisition based on fine-grained semantic variations. The authors also discuss ethical considerations and broader impact of their work, emphasizing the importance of balanced representations across cultural groups and the need to address issues of fairness and equity in language model development.