Does GenAI Rewrite How We Write? An Empirical Study on Two-Million Preprints

TL;DR

This large-scale study investigates how generative AI models influence scientific preprints, revealing accelerated publication cycles, increased linguistic complexity, and disciplinary shifts, thereby shaping the future of scholarly communication.

Contribution

The paper provides the first comprehensive empirical analysis of LLMs' impact on preprint publishing across multiple repositories and disciplines.

Findings

LLMs accelerate submission and revision cycles

Linguistic complexity in preprints has modestly increased

AI-related topics have disproportionately expanded in research

Abstract

Preprint repositories become central infrastructures for scholarly communication. Their expansion transforms how research is circulated and evaluated before journal publication. Generative large language models (LLMs) introduce a further potential disruption by altering how manuscripts are written. While speculation abounds, systematic evidence of whether and how LLMs reshape scientific publishing remains limited. This paper addresses the gap through a large-scale analysis of more than 2.1 million preprints spanning 2016--2025 (115 months) across four major repositories (i.e., arXiv, bioRxiv, medRxiv, SocArXiv). We introduce a multi-level analytical framework that integrates interrupted time-series models, collaboration and productivity metrics, linguistic profiling, and topic modeling to assess changes in volume, authorship, style, and disciplinary orientation. Our findings reveal that LLMs have accelerated submission and revision cycles, modestly increased linguistic complexity, and disproportionately expanded AI-related topics, while computationally intensive fields benefit more than others. These results show that LLMs act less as universal disruptors than as selective catalysts, amplifying existing strengths and widening disciplinary divides. By documenting these dynamics, the paper provides the first empirical foundation for evaluating the influence of generative AI on academic publishing and highlights the need for governance frameworks that preserve trust, fairness, and accountability in an AI-enabled research ecosystem.