Scientific exploration, collaboration and labor division in the large language model era
作者: Xiang Zheng, Xi Hong, Jialin Liu, Chaoqun Ni
分类: cs.DL, cs.AI, cs.CY
发布日期: 2026-07-23
备注: Main text: 21 pages, 4 figures. Supplementary materials: 25 pages, 13 figures, 4 tables
💡 一句话要点
探讨大语言模型时代科学探索与合作的重组
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 大语言模型 科学探索 跨学科合作 团队分工 AI写作 数据分析 科研管理
📋 核心要点
- 现有研究未能充分揭示大语言模型对科学研究策略和团队建设的影响。
- 通过分析科学家的出版和合作历史，探讨LLMs如何促进跨学科研究和团队角色分化。
- 研究发现，自2022年起，科学家的研究领域变得更加多样化，团队分工也趋向细化。
📝 摘要（中文）
大语言模型（LLMs）迅速融入科学工作流程，但其扩散如何影响科学家的研究方向和团队建设尚不明确。本文通过分析775,323名科学家的PubMed Central全文与OpenAlex出版及合作历史，结合137,120篇多作者论文的CRediT贡献声明，发现自2022年起，科学家在更具智力距离的领域发表论文的趋势显著上升，尤其是在非英语国家的成熟科学家中表现突出。AI写作信号强的作者在LLMs广泛应用前已表现出更强的跨学科和探索性，且这一差距在2022年后进一步扩大。此外，研究团队的分工也变得更加细化，团队成员承担更独特的责任，依赖程度降低。整体而言，LLM时代伴随着科学探索、合作及劳动分工的广泛重组。
🔬 方法详解
问题定义：本文旨在探讨大语言模型的扩散如何影响科学家的研究方向和团队合作，现有研究未能揭示这一变化的具体机制。
核心思路：通过结合PubMed Central和OpenAlex的数据，分析科学家的出版和合作历史，揭示LLMs对科学探索和团队分工的影响。
技术框架：研究采用数据挖掘和统计分析的方法，分析775,323名科学家的合作网络和137,120篇论文的贡献声明，重点关注跨学科合作和角色分工的变化。
关键创新：论文的创新在于系统性地将LLMs的影响与科学家研究策略的变化联系起来，揭示了跨学科研究的趋势和团队角色的细化。
关键设计：研究中使用了CRediT贡献声明来分析作者的角色分布，关注角色的流动性和多样性，发现软件和验证角色的增加与概念和管理角色的减少。
🖼️ 关键图片
📊 实验亮点
研究表明，自2022年起，科学家的跨学科研究显著增加，特别是在非英语国家的科学家中。AI写作信号强的作者在跨学科探索方面的优势进一步扩大，团队成员的角色分工变得更加细化，软件和验证角色的比例上升，概念和管理角色则有所下降。
🎯 应用场景
该研究为科学研究的组织和管理提供了新的视角，尤其是在大语言模型的应用背景下。其结果可为科研机构和团队在构建跨学科合作和优化团队分工提供指导，促进科学研究的高效开展。
📄 摘要（原文）
Large language models (LLMs) have rapidly and significantly entered scientific workflows, but it remains unclear how their diffusion is associated with changes in scientists' strategies in research directions and team building. We link PubMed Central full text with OpenAlex publication and collaboration histories for 775,323 scientists and analyze CRediT contribution statements from 137,120 multi-author papers. After 2022, scientists increasingly published across more intellectually distant fields and entered fields in which they had not previously worked. These increases in interdisciplinarity and exploration were especially pronounced among established scientists and scientists from non-English-speaking low- and middle-income countries. Authors with stronger AI-writing signals were already more interdisciplinary and exploratory before the widespread adoption of LLMs, and the gap widened further after 2022 compared with authors with weaker AI-writing signals. Scientists' collaboration networks also became more interdisciplinary after 2022. Yet, among authors with stronger AI-writing signals, research interdisciplinarity was less closely tied to the disciplinary diversity of their collaborators. The division of labor within research teams also became more differentiated. Contributors on papers published after 2022 reported narrower role sets on average, coauthors shared fewer roles in common, and their role profiles became less rigid and more fluid. Software and validation roles increased, while conceptual and management roles decreased. These patterns suggest that team members are taking on more distinct responsibilities and may rely less on one another to perform research tasks. Overall, this study indicates that the LLM era coincides with a broader reorganization of scientific exploration, collaboration, and the division of labor.