大规模多文化知识获取与语言模型基准测试
Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking
AI总结：
本文提出一种大规模多文化知识获取方法，构建了覆盖次国家级地区和民族语言群体的 CultureAtlas 数据集，用于评估语言模型在多元文化背景下的表现并促进文化敏感模型的开发。
摘要：
预训练大型语言模型已经彻底改变了许多应用，但仍然面临与文化偏见以及缺乏文化常识知识相关的挑战，而这些知识对于指导跨文化交流和互动至关重要。认识到现有方法在捕捉世界上丰富多样的文化方面的不足，本文提出了一种新颖的大规模多文化知识获取方法。具体而言，我们的方法策略性地从关于文化主题的信息密集的维基百科文档导航到链接页面的广泛网络。利用这一有价值的数据收集来源，我们构建了 CultureAtlas 数据集，该数据集涵盖了广泛的次国家级地理区域和民族语言群体，并通过数据清洗和预处理来确保文本断言句子的自包含性，以及细粒度的文化特征信息提取。我们的数据集不仅促进了在文化多样性背景下语言模型性能的评估，还作为开发具有文化敏感性和文化意识的语言模型的基础工具。我们的工作标志着在深入理解和弥合人工智能中文化差异方面迈出了重要一步，以促进在数字领域对全球文化进行更具包容性和平衡性的表征。
英文摘要：
Pretrained large language models have revolutionized many applications but still face challenges related to cultural bias and a lack of cultural commonsense knowledge crucial for guiding cross-culture communication and interactions. Recognizing the shortcomings of existing methods in capturing the diverse and rich cultures across the world, this paper introduces a novel approach for massively multicultural knowledge acquisition. Specifically, our method strategically navigates from densely informative Wikipedia documents on cultural topics to an extensive network of linked pages. Leveraging this valuable source of data collection, we construct 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, as well as fine-grained cultural profile information extraction. Our dataset not only facilitates the evaluation of language model performance in culturally diverse contexts but also serves as a foundational tool for the development of culturally sensitive and aware language models. Our work marks an important step towards deeper understanding and bridging the gaps of cultural disparities in AI, to promote a more inclusive and balanced representation of global cultures in the digital domain.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
