LLM让科学写作更复杂，影响全球科研交流。
Large language models reshape the language of science
- 分析2136万篇论文摘要，发现2024年科学语言词汇复杂度上升、句法复杂度下降。
- 非英语母语作者尤其明显，且实验验证LLM会生成此类表达。
- 可能拉大科研与公众语言距离，但助非母语者发表论文。
科学语言是知识生产的核心基础设施，但大型语言模型（LLMs）是否正在改变科学家的写作风格，以及科学知识的传播与可及性仍不明确。本文分析了2020至2024年间发布的2136万篇科学摘要，结合主要期刊的历史记录，追踪科学语言的近期变化。我们识别出2024年出现显著转折点：科学写作的词汇复杂度急剧上升，而句法复杂度下降。这一转变在各学科和期刊层级中普遍存在，且在非英语母语学者中更为明显，尤其是语言类型学上与英语差异较大的背景者。控制性润色实验确认，LLMs会通过偏好词汇密集、句法压缩的表达重现此模式。我们进一步表明这一语言变迁的意义：可能扩大科学话语与公众语言之间的距离，同时帮助非英语母语学者应对英语出版要求。这些发现表明，LLMs正成为科学语言基础设施的新力量，既拓宽了科研作者的参与范围，也削弱了科学传播的可及性。
原文摘要 · Abstract (English)
Scientific language is a central infrastructure of knowledge production, but it remains unclear whether large language models (LLMs) are altering not only how scientists write, but also how scientific knowledge is communicated and accessed. Here we analyze 21.36 million scientific abstracts published between 2020 and 2024, together with historical records from major journals, to trace recent changes in the language of science. We identify a marked turning point in 2024, when scientific writing shows a sharp increase in lexical complexity alongside a decline in syntactic complexity. This shift is pervasive across disciplines and journal tiers, and is more pronounced in texts by scholars working in non-native English contexts, especially those from language backgrounds that differ more typologically from English. Controlled polishing experiments confirm that LLMs reproduce this pattern by favoring more lexically dense and syntactically compressed expression. We further show why this linguistic shift matters: it may widen the distance between scientific discourse and public-facing language, while also helping scholars in non-native English contexts navigate English-language publishing requirements. These findings suggest that LLMs may broaden participation in scientific authorship while narrowing the accessibility of scientific communication, making them a new force in the linguistic infrastructure of science.
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