Dashun Wang

New paper in Nature. The more a government controls its domestic media, the more it dominates AI training data, the more pro-regime outputs we get from AI. By scraping the open web, LLMs are unwittingly laundering state-coordinated narratives into seemingly objective answers.

📄 Excited to share our latest preprint: the first cross-field audit of LLM-hallucinated citations in science ⚠️ Across arXiv, bioRxiv, SSRN & PMC, we estimate 147K fake citations in 2025 alone — threatening both the quality and equity of scientific work.

How are large language models impacting the submission and review process at high-impact journals? Severely. Since the release of ChatGPT in 2022, AI-generated and AI-assisted papers, identified by Pangram, drove a 42% increase in submission volume at Organization Science (figure below). While the journal rejected the majority of these submissions, there is a human cost to reviewing papers, which volunteer reviewers are shouldering. AI-generated content is also showing up in reviews, which similarly suffer in quality because of it -- editors at Organization Science found that AI-generated reviews are lower quality, less specific, and less topically diverse than human-written ones. The problem is not isolated. Earlier this year, ICML desk-rejected 497 papers from authors who submitted AI-generated reviews, after those authors opted into a policy that disallowed the use of AI. Grant funders also saw a surge in applications: the Marie Skłodowska-Curie Actions, a set of major research fellowships for the EU, received 142% more proposals in 2025 compared to 2022. Many scientific and academic systems implicitly rely on friction as a barrier to entry. LLMs have removed that friction, allowing for a deluge of AI slop that is straining the capacity of these institutions.