研究大模型如何改变写作与信息生态，揭示其公平性问题与实际应用效果。
Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems
- 通过算法分析发现大模型在学术、企业等多领域广泛使用。
- 实证显示大模型可为科研论文提供反馈，助早期研究者突破审稿壁垒。
- 指出检测工具可能歧视非主流语言作者，凸显治理中的公平风险。
大型语言模型（LLMs）在改变写作、交流与创作方式方面展现出巨大潜力，社会采纳速度迅速。本文从三个研究方向探讨个人与机构如何适应这一新兴技术：首先，揭示机构采用AI检测工具引入系统性偏差，尤其对非主导语言写作者造成不利影响，凸显人工智能治理中的公平性问题；其次，提出新型群体级算法方法，量化评估大模型在学术同行评审、科学出版物、消费者投诉、企业沟通、职位招聘及国际组织新闻稿等领域的广泛应用，发现其内容中存在一致的AI辅助痕迹；最后，基于大规模实证分析，研究大模型为科研稿件提供反馈的能力，揭示其在帮助早期研究者及资源匮乏群体获得及时反馈方面的潜在价值。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society. This dissertation examines how individuals and institutions are adapting to and engaging with this emerging technology through three research directions. First, I demonstrate how the institutional adoption of AI detectors introduces systematic biases, particularly disadvantaging writers of non-dominant language varieties, highlighting critical equity concerns in AI governance. Second, I present novel population-level algorithmic approaches that measure the increasing adoption of LLMs across writing domains, revealing consistent patterns of AI-assisted content in academic peer reviews, scientific publications, consumer complaints, corporate communications, job postings, and international organization press releases. Finally, I investigate LLMs' capability to provide feedback on research manuscripts through a large-scale empirical analysis, offering insights into their potential to support researchers who face barriers in accessing timely manuscript feedback, particularly early-career researchers and those from under-resourced settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。