让AI理解多元文化，不止是加更多例子，更要尊重不同认知方式。
Plurification in/of language technology -- The integration of culture in next-generation AI
- 用五层技术活动模型系统梳理NLP中文化的表达方式。
- 多数研究只关注输出层面的文化表现，未触及权力与社会背景等深层问题。
- 主张以反思性、多元共存的社科技术路径实现真正的文化对齐。
本文探讨自然语言处理（NLP）中如何将‘文化’具象化，并揭示在技术设计中考虑多元文化背景的可能性与局限。研究表明，仅通过增加‘其他文化’的例子无法实现文化对齐，必须引入多元认识论——即允许多种本地化的知识方式并存。为此，论文采用语言技术设计的社技模型（五层技术活动模型），系统整理了当前NLP中文化相关的研究方法。分析发现，尽管已有研究在提升文化敏感性方面取得进展，但多数方法仍停留在输出或表征层面，未能解决权力结构、治理机制与社会语境等深层议题。论文最终指出，真正实现文化操作化需要超越技术适配，采取一种反思性且多元的社技路径，以应对计算形式化在容纳多元语言与社会文化背景时的潜力与边界。
原文摘要 · Abstract (English)
The paper explores how "culture" can be operationalised in Natural Language Processing (NLP) and what this reveals about the possibilities and limits of considering a plurality of cultural backgrounds in technological design. It proposes that cultural alignment cannot be achieved only by adding more examples of "other cultures", rather it requires plural epistemologies: allowing multiple, locally grounded ways of knowing. To analyze how this plurality of knowing can be addressed in NLP, the paper uses a socio-technical model of language technology (LT) design, the five layers of technological activity model, for collecting and systematizing approaches to culture in NLP. The analysis shows that while NLP research has made progress toward more culturally sensitive systems, many approaches remain partial, addressing "culture" primarily at the level of output or representation while leaving deeper questions of power, governance, and social context unresolved. The paper concludes that operationalising culture requires much more than technical adaptation; it suggests a reflexive and plural socio-technical approach that navigates potentials and limits of computational formalisation for accounting multiple linguistic and socio-cultural backgrounds.
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