AI正在让文化趋于平庸，论文呼吁重新思考如何评估文化差异。
Against 'softmaxing' culture
- 用‘何时是文化’替代‘什么是文化’，聚焦文化出现的具体情境。
- 指出当前机器学习与人机交互评价方法难以捕捉文化复杂性。
- 强调文化普遍性需结合具体语境，反对抽象化概括。
人工智能正在使文化趋于同质化。对‘文化’的评估揭示出大型AI模型正将丰富的语言差异简化为通用表达，这一现象被称为‘软化文化’（softmaxing culture），是当前AI评估面临的核心挑战之一。提升文化评估能力对于实现大模型的文化对齐至关重要。本文认为，机器学习与人机交互领域的评估方法存在局限。提出两个关键概念转变：其一，评估不应始于‘什么是文化’，而应从‘何时是文化’切入；其二，尽管承认文化普遍性的哲学观点，但关键在于将其置于具体情境中理解。这两大转变推动评估视角从技术标准转向更契合文化复杂性的方法。
原文摘要 · Abstract (English)
AI is flattening culture. Evaluations of "culture" are showing the myriad ways in which large AI models are homogenizing language and culture, averaging out rich linguistic differences into generic expressions. I call this phenomenon "softmaxing culture,'' and it is one of the fundamental challenges facing AI evaluations today. Efforts to improve and strengthen evaluations of culture are central to the project of cultural alignment in large AI systems. This position paper argues that machine learning (ML) and human-computer interaction (HCI) approaches to evaluation are limited. I propose two key conceptual shifts. First, instead of asking "what is culture?" at the start of system evaluations, I propose beginning with the question: "when is culture?" Second, while I acknowledge the philosophical claim that cultural universals exist, the challenge is not simply to describe them, but to situate them in relation to their particulars. Taken together, these conceptual shifts invite evaluation approaches that move beyond technical requirements toward perspectives that are more responsive to the complexities of culture.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。