14%论文都有AI代写？Nature：每7篇就有1篇藏有ChatGPT特征词

Core Insights - The article discusses the increasing prevalence of AI-assisted writing in biomedical research, highlighting that over 20% of the 1.5 million abstracts published on PubMed in 2024 exhibit characteristics typical of large language models (LLMs) [1][3]. Group 1: AI Usage in Biomedical Research - A significant portion of biomedical papers, approximately 14%, has been identified as having been written with the assistance of LLMs within a year [1]. - The use of LLMs in certain countries and disciplines has surpassed 20%, indicating a growing trend in AI-assisted writing [3][5]. - The study found that 10%-11% of abstracts in 2024 utilized LLMs, with some sub-corpora showing rates as high as 30% [11][15]. Group 2: Characteristics of LLM Writing - LLMs tend to favor stylistic verbs and adjectives that do not contribute to the content but alter the writing style, leading to the frequent use of words like "intricate" and "notably" [2][11]. - The analysis revealed that 66% of the overused words in 2024 were verbs, while 16% were adjectives, indicating a preference for certain types of language [11][21]. - Authors are beginning to adjust their writing to avoid obvious LLM markers, which complicates the assessment of LLMs' impact on academic output [5][21]. Group 3: Variability in LLM Usage - The reliance on LLMs varies significantly across disciplines, with fields like computational biology and bioinformatics seeing around 20% usage due to rapid technological advancements [15][16]. - In non-English speaking countries, such as China and South Korea, LLM usage can reach 15% as researchers seek assistance with English writing [16]. - Open-access journals, particularly those with simpler review processes, show higher LLM usage rates (up to 24%) compared to prestigious journals like Nature and Science, which have rates between 6% and 8% [16]. Group 4: Detection and Adaptation - Researchers are exploring methods to detect LLM-generated text, but current detection tools may not accurately differentiate between human and AI-generated content [27][28]. - The study indicates that while authors can modify their writing to reduce LLM characteristics, complete avoidance is challenging [25][28]. - Future research aims to quantify the impact of AI on academic literature more comprehensively, moving beyond single text analyses to broader statistical evaluations [28].