研究：低资源语言研究增长但语言包容性未同步提升
原标题：Measuring Research Progress and Linguistic Inclusion in Low-Resource Languages
AI 摘要
该研究分析了2020至2025年间1158篇同行评审的低资源语言（LRL）开放获取论文，发现相关论文年发表量从52篇增至415篇，但增长主要集中在已有代表性的语言上，而非扩展覆盖不足的语言。研究提出多维度的“语言包容性框架”（LIF），从研究增长、代表性扩展、代表性连续性和分布公平四个维度衡量包容性，并指出亚洲语言占所分析LRL使用者的64.1%，却仅获得46.7%的语言代表性份额，而欧洲语言获得其比例份额的5.3倍。研究结论认为，仅靠研究增长不足以衡量语言技术公平包容的进展。
正文节选
Measuring Research Progress and Linguistic Inclusion in Low-Resource Languages 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