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研究揭示低资源语言NLP领域存在显著语言包容性失衡

一项发表于2026年10月24日arXiv的预印本研究分析了2020至2025年间1158篇开放获取论文,提出多维‘语言包容性框架(LIF)’,发现尽管ACL Anthology中相关论文数量增长5.4倍,但语言覆盖严重偏向已代表语言;亚洲语言使用者占低资源语言总人口64.1%,仅获46.7%表征份额,而欧洲语言获5.3倍其人口比例的表征。

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Measuring Research Progress and Linguistic Inclusion in Low-Resource Languages

arXiv:2610.30296v1 Announce Type: new Abstract: Large Language Models (LLMs) have accelerated research on low-resource and multilingual NLP, but their effectiveness in creating an equitable landscape remains unmapped. We analyze 1,158 peer-reviewed open-access papers published between 2020 and 2025, examining research attention to low-resource languages (LRLs) and representation across language and speaker populations. To assess linguistic inclusion beyond publication counts, we introduce the Linguistic Inclusion Framework (LIF), a multidimensional framework that measures linguistic inclusion in four dimensions: research growth, representation expansion, representation continuity, and distributional equity. While annual publications grew from 52 to 415, a 5.4X increase in their ACL Antholo

Measuring Research Progress and Linguistic Inclusion in Low-Resource Languages
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