Exploring the Structure of AI-Induced Language Change in Scientific English

📄 arXiv: 2506.21817v1 📥 PDF

作者: Riley Galpin, Bryce Anderson, Tom S. Juzek

分类: cs.CL, cs.AI

发布日期: 2025-06-26

备注: Accepted and published at FLAIRS 38. 8 pages, 4 figures, 1 table. Licensed under CC BY-NC-SA 4.0

期刊: The International FLAIRS Conference Proceedings (Vol. 38), 2025

DOI: 10.32473/flairs.38.1.138958

💡 一句话要点

探讨AI引发的科学英语语言结构变化

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 科学英语 语言变化 大型语言模型 语义分析 词性标注 同义词研究 语言技术

📋 核心要点

  1. 科学英语的语言变化速度快且复杂，现有研究未能清晰揭示其结构特征。
  2. 本研究通过分析词性标注和同义词群体，探讨语言变化的语义和语用层面。
  3. 研究发现，语义群体的整体变化模式表明，语言技术对人类语言的影响是深远的。

📝 摘要（中文）

近年来，科学英语经历了快速而前所未有的变化，诸如“delve”、“intricate”和“crucial”等词汇自2022年以来频率显著上升。这些变化普遍归因于大型语言模型（如ChatGPT）在偏见和不对齐话题中的影响。然而，除了频率变化外，这些语言变化的具体结构仍不清晰。本研究探讨这些变化是否涉及同义词被“激增词”替代，或反映更广泛的语义和语用变化。我们通过词性标注分析，量化语法类别的语言变化，发现整个语义群体通常一起变化，表明大型语言模型引发的变化主要是语义和语用的，而非单纯的词汇变化。

🔬 方法详解

问题定义：本研究旨在揭示AI引发的科学英语语言变化的具体结构，现有研究主要集中在频率变化，缺乏对变化机制的深入分析。

核心思路：通过对科学文献中词汇使用频率的系统分析，结合词性标注，探讨同义词的替代关系及其语义变化，揭示语言变化的深层次原因。

技术框架：研究首先收集PubMed中的科学摘要数据，进行词频统计和词性标注，随后分析同义词群体的变化趋势，最后对“崩溃”词汇进行深入分析。

关键创新：本研究的创新点在于系统性地分析了同义词群体的整体变化，发现语义群体的变化模式与传统的词汇变化理论存在显著差异。

关键设计：在分析过程中，采用了词性标注技术，区分名词和形容词的使用情况，确保对语言变化的全面理解。

📊 实验亮点

研究发现，多个同义词群体的使用频率呈现出同步上升的趋势，尤其是“重要”一词的使用显著下降，表明语言变化的复杂性和动态性。这一发现为理解语言技术的影响提供了新的视角。

🎯 应用场景

该研究为理解AI技术如何影响科学交流提供了重要视角，潜在应用于语言模型的优化、科学写作的指导以及语言教育等领域，具有重要的实际价值和未来影响。

📄 摘要（原文）

Scientific English has undergone rapid and unprecedented changes in recent years, with words such as "delve," "intricate," and "crucial" showing significant spikes in frequency since around 2022. These changes are widely attributed to the growing influence of Large Language Models like ChatGPT in the discourse surrounding bias and misalignment. However, apart from changes in frequency, the exact structure of these linguistic shifts has remained unclear. The present study addresses this and investigates whether these changes involve the replacement of synonyms by suddenly 'spiking words,' for example, "crucial" replacing "essential" and "key," or whether they reflect broader semantic and pragmatic qualifications. To further investigate structural changes, we include part of speech tagging in our analysis to quantify linguistic shifts over grammatical categories and differentiate between word forms, like "potential" as a noun vs. as an adjective. We systematically analyze synonym groups for widely discussed 'spiking words' based on frequency trends in scientific abstracts from PubMed. We find that entire semantic clusters often shift together, with most or all words in a group increasing in usage. This pattern suggests that changes induced by Large Language Models are primarily semantic and pragmatic rather than purely lexical. Notably, the adjective "important" shows a significant decline, which prompted us to systematically analyze decreasing lexical items. Our analysis of "collapsing" words reveals a more complex picture, which is consistent with organic language change and contrasts with the patterns of the abrupt spikes. These insights into the structure of language change contribute to our understanding of how language technology continues to shape human language.