分析34种语言新闻中AI引发的词汇变化，发现全球语言正趋向统一表达。
AI-Associated Lexical Shifts Across 34 Languages: Cross-Lingual Convergence and Diachronic Uptake in News Writing
- 用对比GPT-4.1与真人写作的方法，识别每种语言中被过度使用的AI词汇
- 26种语言的AI相关词汇使用率平均上升15.1%，而普通词汇反而下降4.5%
- 跨语言出现相似高频词，如'强调'类动词在24种语言中反复出现
AI相关词汇变化此前主要在科学英语中被记录。本研究扩展至WMT新闻爬取语料中的34种语言，改进了分半延续性诊断方法，通过对比GPT-4.1生成文本与人工标注基准文本，计算每种语言中词汇的对数流行度比值，得出被过度使用的词干排名。研究发现显著的跨语言语义趋同：在类型学差异大的语言中，语义相关概念反复出现，'emphasize'-类动词出现在24种语言中。嵌入分析与人工验证均支持该模式。进一步考察自ChatGPT发布以来新闻写作的历时演变，追踪各语言前20个高使用频率的AI相关词汇，发现2020–2021年到2023–2024年间，26种语言的使用率上升，平均增幅达+15.1%，而对照基准词仅下降-4.5%。在10种具有更长历史数据的语言中，2022年后增长显著高于前期，但效应量仍小于科学英语。研究通过多种种子、模型变体、数据规模和模型家族进行了充分验证，结果表明AI相关的词汇偏好已超越英语，可能对全球语言使用产生跨语言同质化影响。
原文摘要 · Abstract (English)
AI-associated lexical shifts have been documented mainly in Scientific English. We extend this work to 34 languages in the WMT News Crawl corpus, refining a split-halves continuation diagnostic that compares GPT-4.1 continuations with matched human gold-standard text. For each language, we derive ranked AI-overused lemmas using log prevalence ratios. We find substantial cross-lingual semantic convergence: semantically related concepts recur across typologically diverse languages, with 'emphasize'-type verbs appearing in 24 of 34 languages. Embedding-based and manual analyses support this pattern. We also examine diachronic uptake in news writing before and after ChatGPT's release. Tracking each language's top 20 AI-overused items, we find prevalence increases in 26 of 34 languages from 2020-2021 to 2023-2024, with a mean change of +15.1%, whilst matched baseline words show no comparable increase (-4.5%). In 10 languages with longer historical coverage, longitudinal analyses show post-2022 increases that exceed the modest shifts observed in earlier periods, though with smaller effect sizes than in Scientific English. We validate our approach extensively, including across seeds, model variants, data sizes, model families, and more. Our findings are consistent with the view that AI-associated lexical preferences extend beyond English and may exert cross-lingual homogenising pressure on global language use.
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