超越Via：分析和估计大语言模型在学术论文中的影响
Beyond Via: Analysis and Estimation of the Impact of Large Language Models in Academic Papers
AI总结：
研究通过分析arXiv论文，发现大语言模型影响了学术论文中的词汇使用，如标题中'beyond'和'via'的增加，以及摘要中'the'和'of'的减少，揭示了LLM使用在现实中的异质性和动态性。
摘要：
通过分析arXiv论文，我们报告了几个词汇使用的变化，这些变化很可能由大语言模型（LLMs）驱动，但此前未得到充分关注，例如标题中'beyond'和'via'的频率增加，以及摘要中'the'和'of'的频率减少。由于不同LLM之间的相似性，实验显示当前分类器在多类分类任务中难以准确确定生成特定文本的特定模型。同时，LLM之间的差异也导致了学术论文中词汇使用模式的演变。通过采用直接且高度可解释的线性方法，并考虑模型和提示之间的差异，我们定量评估了这些影响，并表明现实中的LLM使用是异质且动态的。
英文摘要：
Through an analysis of arXiv papers, we report several shifts in word usage that are likely driven by large language models (LLMs) but have not previously received sufficient attention, such as the increased frequency of "beyond" and "via" in titles and the decreased frequency of "the" and "of" in abstracts. Due to the similarities among different LLMs, experiments show that current classifiers struggle to accurately determine which specific model generated a given text in multi-class classification tasks. Meanwhile, variations across LLMs also result in evolving patterns of word usage in academic papers. By adopting a direct and highly interpretable linear approach and accounting for differences between models and prompts, we quantitatively assess these effects and show that real-world LLM usage is heterogeneous and dynamic.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
