27%的母语者超百万语言在大模型中完全隐形，揭示了AI中的结构性语言不公。
Invisible Languages of the LLM Universe
- 用语言活力与数字存在度双维度划分全球语言现状
- 发现27%高活力语言近乎零数字存在，构成'隐形巨族'
- 指出英语主导非技术必然，而是殖民权力结构延续
大型语言模型基于海量多语言语料训练，但这一丰沛掩盖了深刻危机：全球7,613种活语言中，约2,000种拥有数百万使用者的语言在数字生态中几乎不可见。本文提出一个融合实证测量与后殖民理论的分析框架，解释为何人工智能中的语言不平等并非偶然，而是结构性的。通过对所有已记录人类语言的数据分析，我们识别出四类语言：强固区（33%，高活力与高数字存在）、数字回响（6%，高数字存在但活力衰退）、消逝之声（36%，低活力与低数字存在），以及关键的隐形巨族（27%，高活力但近乎零数字存在）。这些语言虽有数百万母语者却未进入大模型宇宙。分析表明，这些模式反映了从殖民时期语言等级制到当代人工智能开发的延续，构成数字认知不公。研究揭示，英语在人工智能中的主导地位并非技术必需，而是权力结构系统性排除边缘语言知识的结果。论文最后探讨了去殖民化语言技术与民主化人工智能受益的路径。
原文摘要 · Abstract (English)
Large Language Models are trained on massive multilingual corpora, yet this abundance masks a profound crisis: of the world's 7,613 living languages, approximately 2,000 languages with millions of speakers remain effectively invisible in digital ecosystems. We propose a critical framework connecting empirical measurements of language vitality (real world demographic strength) and digitality (online presence) with postcolonial theory and epistemic injustice to explain why linguistic inequality in AI systems is not incidental but structural. Analyzing data across all documented human languages, we identify four categories: Strongholds (33%, high vitality and digitality), Digital Echoes (6%, high digitality despite declining vitality), Fading Voices (36%, low on both dimensions), and critically, Invisible Giants (27%, high vitality but near-zero digitality) - languages spoken by millions yet absent from the LLM universe. We demonstrate that these patterns reflect continuities from colonial-era linguistic hierarchies to contemporary AI development, constituting digital epistemic injustice. Our analysis reveals that English dominance in AI is not a technical necessity but an artifact of power structures that systematically exclude marginalized linguistic knowledge. We conclude with implications for decolonizing language technology and democratizing access to AI benefits.
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。