AI 中文总结
该研究提出LLM输出具有个体方言本质，通过分析两个LLM生成文本数据集，发现模型间存在独特语言特征，该视角对多领域研究有潜在价值。
AI 中文摘要
尽管大语言模型（LLM）的输出常被作为统称为“AI语言”的集体超级变体分析，但本章提出，这种视角与类似人类个体方言（idiolect）的、特定模型独有的语言特征并存。我们分析了两个关于LLM生成社会主题文本的数据集：2024年由六个模型构成的语料库（Improta等人2024年），以及使用相同提示词生成的2026年新语料库，该语料库包含六个当代模型。我们利用计算描述符和风格计量主成分分析的研究结果显示，2024年与2026年模型群体的风格存在代际转变，同时证明每个单独模型都保持着独特的语言特征。这种多层互动通过 contraction 频率得以体现，在2026年的同一模型群体中，每百万词的 contraction 频率从超过1200到超过30000不等。最终我们得出结论：将LLM输出视为个体方言本质的观点提供了一个有价值的框架，对变异与变化研究、LLM生成文本检测、法医语言学以及基于使用的语言方法具有潜在意义。
英文摘要
While large language model outputs are frequently analysed as a collective super variety termed "AI language," this chapter argues that this perspective coexists with distinct, model-specific linguistic signatures akin to human idiolects. We analyse two datasets of LLM-generated texts on societal topics: a 2024 corpus of six models (Improta et al. 2024) and a newly generated 2026 corpus using the same prompts featuring six contemporary models. Our findings, utilising computational descriptors and stylometric principal component analysis reveal a generational shift between the style of the 2024 and 2026 cohorts, while demonstrating that each individual model maintains a unique linguistic profile. This multi-layered interplay is illustrated by contraction frequencies, which vary from over 1,200 to over 30,000 per million words within the same cohort of models (2026). Ultimately, we conclude that treating LLM output as idiolectal in nature provides a valuable framework with potential implications for research on variation and change, LLM-generated text detection, forensic linguistics and usage-based approaches to language.
Comments33 pages, 6 figures, 6 tables. Submitted as a chapter to the post-workshop volume "Corpus Linguistics 2040" (Digital Linguistics series)
