元文化能力：攀登文化意识的正确山丘
Meta-Cultural Competence: Climbing the Right Hill of Cultural Awareness
AI总结：
本文针对LLM的西方中心偏见，通过扩展Octopus test提出AI系统应具备可泛化至陌生文化的元文化能力，并给出其原则及衡量、建模思路。
摘要：
近期大量研究表明，Large Language Models（LLM，大语言模型）偏向西方和以盎格鲁为中心的世界观，这削弱了它们在非西方文化环境中的实用性。然而，“文化”是一个复杂、多面的主题，其在LLM及基于LLM的应用中的意识、表征和建模可以通过多种方式定义和衡量。在这篇立场论文中，我们追问LLM具备“文化意识”意味着什么；并通过一个思想实验——它是Bender和Koller（2020）提出的Octopus test的扩展——论证LLM及基于LLM的AI系统真正需要的并非文化意识或文化知识，而是元文化能力，这才能使其在各种文化（包括完全未见过的文化）中发挥作用。我们阐述了元文化能力AI系统的原则，并讨论了衡量和建模这些原则的方法。
英文摘要：
Numerous recent studies have shown that Large Language Models (LLMs) are biased towards a Western and Anglo-centric worldview, which compromises their usefulness in non-Western cultural settings. However, "culture" is a complex, multifaceted topic, and its awareness, representation, and modeling in LLMs and LLM-based applications can be defined and measured in numerous ways. In this position paper, we ask what does it mean for an LLM to possess "cultural awareness", and through a thought experiment, which is an extension of the Octopus test proposed by Bender and Koller (2020), we argue that it is not cultural awareness or knowledge, rather meta-cultural competence, which is required of an LLM and LLM-based AI system that will make it useful across various, including completely unseen, cultures. We lay out the principles of meta-cultural competence AI systems, and discuss ways to measure and model those.
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。
