89%的生物医学论文含大模型写作痕迹，讨论部分使用率更高。
Most biomedical publications show signs of LLM-assisted writing
- 通过词频变化检测大模型辅助写作，避免传统方法偏差。
- 2025年底89%生物医学论文显示大模型用词特征，方法部分超50%。
- 适合关注学术诚信与生成内容监管的研究者参考。
近年来，基于大语言模型的聊天机器人和智能体广泛用于学术写作。虽然能突破语言障碍，但也引发学术不端与欺诈担忧。为支持政策制定，需准确监测学术文献中大模型改写文本的普遍性。尽管已有相关进展，现有方法仍无法提供可靠估计。本文提出并验证了一种基于词频变化的新无偏估计方法，应用于PubMed Central的开放获取生物医学论文全文。结果显示，截至2025年底，89%的论文表现出大模型相关的词汇特征。在讨论部分，大模型使用率达68%，是方法部分（32%）的两倍；即便在方法部分，整体使用率也超过50%。我们认为这些估计对制定未来学术规范与政策至关重要。
原文摘要 · Abstract (English)
Over the past several years, LLM-powered chatbots and agents have become widely used as a tool for academic writing. LLM-assisted writing can be valuable by removing language barriers but at the same time causes concerns about misconduct and fraud. To inform policy decisions, it is necessary to monitor the prevalence of LLM-altered texts in scholarly publications. Despite some recent progress in this direction, no existing method can produce reliable estimates. Here we suggest and validate a new unbiased approach to estimate LLM usage in a corpus of texts based on changing word frequencies. We apply our method to the full texts of open-access biomedical papers from Pubmed Central, and show that by the end of 2025, 89% of papers show excess of LLM-associated vocabulary. We also find that LLMs are twice as likely to be used when writing a paragraph in the Discussion section (68%) compared to a paragraph in the Methods section (32%), but even inside the Methods section, the overall prevalence of LLM usage is over 50%. We believe that our estimates are crucial to shape future guidelines and policies.
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